Zaven & Sonia Akian College of Science and Engineering (ACSE) Course Descriptions
Program: BSCS
Course Code: CS100
Title: Calculus 1
Description: This introductory course covers topics including: functions of one variable, transcendental functions; introduction to complex numbers; polar coordinates; limits, continuity; derivatives, techniques of differentiation, differentiability, extrema of differentiable functions, applications of differentiation; indefinite and definite integrals, mean value theorem, related-rates problems, and the fundamental theorem of calculus. Students are required to complete weekly problem sets in order to develop basic proficiency in the mathematical foundations introduced in the field of Calculus. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS101
Title: Calculus 2
Description: This course builds on CS100 and covers topics including: the definite (Riemann) integral, applications of integrals, improper integrals, numerical series, Taylor series. Students are required to complete weekly problem sets in order to develop proficiency on the subject. The format of the course is three hours of instructorled class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS100 or equivalent
Corequisites:
Program: BSCS
Course Code: CS102
Title: Calculus 3
Description: This final course in the three-term Calculus sequence spans the following topics: vectors in multiple dimensions; functions of several variables, continuity, partial derivatives, the gradient and Jacobian, directional derivatives, extrema, Taylor’s Theorem, Lagrange multipliers; multiple integrals, line integrals, surface integrals, divergence theorem, Green’s theorem, Stokes’ theorem. Students are required to complete weekly problem sets in order to demonstrate intermediate competency in multi-variable Calculus. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS101
Corequisites:
Program: BSCS
Course Code: CS103
Title: Real Analysis
Description: The fundamental concepts in analysis are rigorously treated with emphasis on reasoning and proofs. The topics include completeness and order properties of real numbers, limits, continuity and uniform continuity, conditions for integrability and differentiability, infinite sequences and series, basic concepts of topology and measure, metric spaces, compactness, connectedness, continuous functions on a compact set, the contraction mapping lemma. Students are required to apply practical analytical methods to formulate, critically assess, and solve problems which arise in computational sciences and mathematical modeling. Three hours of instructorled class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS102 or equivalent
Corequisites:
Program: BSCS
Course Code: CS104
Title: Linear Algebra
Description: This introductory course covers topics including: vectors, dot products, hyperplanes; systems of linear equations, Gaussian elimination; matrix operations, determinants; vector spaces, linear independence, change of basis, eigenvectors and eigenvalues, the characteristic equation; the spectral theorem; complex vector spaces, complex eigenvalues, Jordan canonical form, matrix exponentials, differential equations. Students are required to apply practical analytical methods to solve problems which arise in computational sciences. Students will also learn to formulate a matrix representation of basic problems seen in mathematical modeling.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS105
Title: Ordinary Differential Equations
Description: The course examines topics including: first order equations, solution methods, higher order linear equations, series solutions, Laplace transforms, systems of linear equations, linear systems with constant coefficient, systems with periodic coefficients, existence and uniqueness of solutions, phase plots, eigenvalue problems, eigenfunction expansions, Sturm-Liouville theory, linearization about critical points, limit cycles, Poincaré-Bendixson theorem, Hartman-Grobman theorem, chaotic solutions and strange attractors, applications. Through the course, students will learn to formulate representations of basic problems seen in mathematical modeling. Students are required to apply practical analytical methods to solve problems which arise in computational sciences. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS101, CS104
Corequisites:
Program: BSCS
Course Code: CS107
Title: Probability
Description: This course is an introduction to the mathematical study of randomness and uncertainty. Course covers topics including: Axioms and properties of probability; Conditional probability and independence of events; Random variables and distribution functions; Expectation, variance and covariance; Jointly distributed random variables; Independent random variables; The law of large numbers; The central limit theorem; Markov chains. Students are required to complete weekly problem sets in order to develop problem solving skills in Probability. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS101, CS111
Corequisites:
Program: BSCS
Course Code: CS108
Title: Statistics
Description: This course provides students with a general introduction to statistical modeling and inference, including topics such as descriptive statistics, estimation in parametric models, risk evaluation, maximum likelihood method and method of moments, Bayesian approach, confidence intervals, statistical hypotheses testing, multiple linear regression, least-squares estimation, significance of the coefficients, goodness-of-fit tests, and chi-squared test of independence. Students will develop basic skills in data modeling and gain proficiency in R software. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites: CS107
Corequisites:
Program: BSCS
Course Code: CS110
Title: Introduction to Computer Science
Description: The course provides students with a broad foundation in computer science. Topics include: introduction to digital technology, historical review from valves to integrated circuits; logic gates; binary, octal, and hexadecimal systems; evolution of computer architecture, Von Neumann architecture, basic components, internal and external interfaces, types of removable media; introduction to operating systems. Students should be able to demonstrate basic understanding of the software and hardware systems related to computational sciences, and demonstrate strong understanding of the relevant common software and information technology. Students will develop rudimentary foundational knowledge in mathematical modeling and gain proficiency using software and hardware systems related to computational science. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS111
Title: Discrete Mathematics
Description: This is an introduction to discrete mathematics and discrete structures. The course examines topics including: propositional logic; Boolean algebra; introduction to set algebra; infinite sets; relations and functions; recurrences; proof techniques; introduction to number theory; elementary combinatorics and graph theory; applications to computer science. Students will learn to apply discrete numerical methods to solve problems which arise in computational sciences. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS112
Title: Numerical Analysis
Description: The course investigates topics including: floating-point arithmetic, cancellation and rounding, random number generation; finding of roots of nonlinear equations and systems; interpolation, extrapolation, function approximation; numerical integration, Gaussian quadrature; Monte-Carlo methods; numerical solutions of ordinary differential equations, predictor-corrector methods, shooting methods for boundary value problems. Students are required to formulate, critically assess, and apply practical numerical methods to solve problems and subtasks. Through the problem sets and group projects, students will demonstrate intermediate proficiency in designing and analyzing complex data structures and algorithms as well as in developing and testing software tools and methods relevant to numerical analysis. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS104, CS101 or equivalent
Corequisites:
Program: BSCS
Course Code: CS120
Title: Introduction to Object-Oriented Programming
Description: The course will survey the following topics: control structures, functions, arrays, strings, introduction to UML, classes and data abstraction, inheritance, introduction to polymorphism, abstract classes and interfaces. Students are required to develop basic proficiency in utilizing and testing software systems related to computational sciences and in applying at least one programming language to software development. Three hours of instructorled class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS110
Corequisites:
Program: BSCS
Course Code: CS121
Title: Data Structures
Description: The course explores topics including: basic object-oriented programming principles; linear and non-linear data structures – linked lists, stacks, queues, trees, tables and graphs; dynamic memory management; design of algorithms and programs for creating and processing data structures; searching and sorting algorithms. Students are required to complete programming projects in which they design, analyze, and develop complex data structures in at least one programming language. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS111, CS120
Corequisites:
Program: BSCS
Course Code: CS125
Title: Logic for Computer Science
Description: This course provides an introduction to logic from a computational perspective. Students learn to formalize information as logical statements, reason rigorously with those statements, and apply logic-driven tools to solve complex problems in mathematics, science, engineering, and business. The course also shows how formal logic underlies automated methods for uncovering critical errors in both software and hardware systems. Instructor-led class time including discussions and problem sets.
Credits: 3.0
Prerequisites: CS104, CS111, CS120
Corequisites:
Program: BSCS
Course Code: CS130
Title: Computer Organization
Description: Functional organization and operation of digital computers. Coverage of assembly language; addressing, stacks, argument passing, arithmetic operations, decisions, macros, modularization, linkers, debuggers. Device drivers will be considered. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: CS120 OR ENGS110
Corequisites:
Program: BSCS
Course Code: CS131
Title: Human Computer Interaction (HCI)
Description: The topics include: concepts of human computer interaction, techniques for user interface design; user-centered design, interface development techniques, usability evaluation; overview of interface devices and metaphors; visual development environments, other development tools. Students should be able to demonstrate advanced knowledge of software and hardware systems related to computational sciences. Students should also be able to formulate and critically assess problems and sub-tasks including identification of sources and investigative techniques related to the field. Students are required to complete group projects in which they formulate, critically assess, and investigate problems relating to software and hardware systems. Students will complete formal presentations in order to develop experience communicating to audiences both within and outside the discipline. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS132
Title: Theory of Communication Networks
Description: The course investigates several communication problems in networks; one-to-all, all-to-all, one-to-many. Specific communication models are considered by placing constraints on the sets of messages, senders, and receivers, on the network’s topology, on the rules that govern message transmissions, and on the amount of information about the network known to individual network members. One goal is to design network structures which are inexpensive to construct yet allow fast communication. The second major goal is to design efficient communication algorithms for commonly used networks under different communication models. These require knowledge of graph theory, combinatorics, and design and analysis of algorithms. The students are required to complete theoretical problem sets and proofs in order to develop advanced knowledge of efficient communication algorithms and combinatorial properties of certain types of networks. Students will also complete and present in class a project based on recent research articles in order to develop advanced knowledge and research skills to formulate and investigate real research problems in the future. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS140
Title: Mechanics
Description: This course introduces students to classical mechanics. Topics include: space and time; straight-line kinematics; motion in a plane; forces and static equilibrium; Newton’s laws; particle dynamics, with force and conservation of momentum; angular motion and conservation of angular momentum; universal gravitation and planetary motion; collisions and conservation laws; work, potential energy and conservation of energy; vibrational motion; conservative forces; inertial forces and non-inertial frames; central force motions; rigid bodies and rotational dynamics. Students are required to complete weekly problem sets in order to develop problem solving skills in Probability. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS101
Corequisites:
Program: BSCS
Course Code: CS201
Title: Complex Analysis
Description: The course examines the theory of functions of one complex variable. The topics include complex numbers, complex functions, differentiability, Cauchy-Riemann equations, analytical functions; complex integration, the Cauchy integral formula, calculation of residues, Liouville’s theorem, the Gauss mean value theorem, the maximum modulus theorem, Rouche’s theorem, the Poisson integral formula; Taylor-Laurent series; singularity theory; analytical continuation; elliptic functions; conformal mapping, applications to ODEs and PDEs. Students are required to complete weekly problem sets and proofs in order to develop advanced knowledge of analyticalal methods. Students will learn to utilize advanced methods to formulate, assess, and solve problems and subtasks in computational science as well as across a broad range of disciplines. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS202
Title: Functional Analysis
Description: The course gives an introduction to functional analysis, which is a branch of mathematics in which one develops analysis in infinite dimensional vector spaces. The main areas to be covered are normed spaces with an emphasis on Banach and Hilbert spaces. Students will be introduced to fundamental theorems related to Banach spaces: The Hahn-Banach, Fixed point, Uniform Boundedness Principle, Open Mapping and Closed Graph theorems. This course will provide also an overview of Spectral theory for compact operators with applications in integral and differential equations. Instructor-led class time including discussions and problem sets; assessment by exams and problem sets.
Credits: 3.0
Prerequisites: CS103
Corequisites:
Program: BSCS
Course Code: CS205
Title: Partial Differential Equations
Description: An introductory course into Partial Differential Equations (PDEs) which outlines analytical procedures for solving PDEs that arise from mathematical modeling of physical phenomena such as wave propagation, heat and mass transfer and electric potential discharge, to shape processing and motion/jump simulations in video gaming. The class will cover different classifications and orders of PDEs such as 2nd order elliptic and 1st and 2nd order hyperbolic equations, and will be introduce corresponding solution methodologies such as the method of characteristics, separation of variables and Laplace Transforms. The course will primarily deal with analytical methods but will include a small section on numerical algorithms for solving simple PDEs. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS105
Corequisites:
Program: BSCS
Course Code: CS211
Title: Introduction to Algorithms
Description: The course surveys topics including: review of main abstract data types; sorting algorithms, correctness, space and time complexity; hashing and hash tables, collision resolution strategies; graph algorithms; divide-and-conquer algorithms, dynamic programming; NP-completeness. Students are required to critically analyze, formulate and solve problems using analytical knowledge related to algorithms. Students should also be able to display proficiency in designing and analyzing complex algorithms and understand the software relevant to this field. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS213
Title: Optimization
Description: The course explores the following topics: optimization problems; dogleg and hookstep methods; simulated annealing; approximation algorithms; introduction to game theory; scheduling; basic optimization models in financial markets; nonlinear continuous optimization; conjugate gradient methods, Newton-type methods. Through the course, students will develop the ability to critically analyze and solve problems using advanced knowledge related to optimization and contemporary methods in optimization techniques. Students will also develop proficiency in designing and analyzing complex data structures and algorithms. Additionally, students are required to complete individual projects in order to develop their ability to discover and learn relevant material on their own. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS102, CS112
Corequisites:
Program: BSCS
Course Code: CS215
Title: Cryptography
Description: Introduction of basic principles and methods of modern applied cryptography. Demonstration how cryptography can help to solve information security problems and our focus will be basically internet security.
Credits: 3.0
Prerequisites: CS211
Corequisites:
Program: BSCS
Course Code: CS216
Title: Web Application Development
Description: This course provides a comprehensive introduction to the fundamental concepts and practical techniques involved in developing web applications. Students will explore key topics including front-end and back-end development, client-server architecture, and the integration of databases with web technologies. The course focuses on building proficiency in full-stack development while reinforcing coding best practices, user experience design, and secure application deployment. Instructor-led class time including problem sets as well as project-based assessments.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS217
Title: Computer Graphics
Description: The course provides students with theoretical and applied tools in graphics development. The course examines topics including: geometric concepts, such as tangent plane, normal vector; pixel-related operations; interactive methods, such as mouse and keyboard callback functions; representation of graphics primitives; general introduction to Open GL as a State Machine; various shading algorithms to illustrate the rendering process; color calculations; texturing. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS102, CS221
Corequisites:
Program: BSCS
Course Code: CS218
Title: Game Development
Description: This course is an introduction to video game design and development. It equips students with the programming skills necessary to create simple game programs in one or more programming languages. Students will be exposed to the latest techniques and tools to add functionality to game objects, create playable characters, design Enemy AI, add responsive UI, create and control animations, trigger visual effects, and combine all elements to create a working game. By the end of the course, students will go through every step of creating a game and be armed with the necessary knowledge to develop their projects. Assessment may include class participation, papers, essays, quizzes, exams, projects, and presentations.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS219
Title: Mobile Application Developement
Description: This course will introduce mobile application development covering topics such as current technologies, development environments, frameworks and programming languages, application testing and debugging tools and mechanisms, required libraries introduction, best practices on mobile development, monetization of applications, monitoring and alerting instruments, and data collection. The course will include hands-on sessions using modern technologies to design and develop user interfaces with simple interactivity, and publish the applications.
Students will design and build a variety of applications throughout the course to reinforce the concepts being taught and to help students practice what they are learning. Instructor-led lectures and discussions; assessment may include problem sets, software implementation, exams, and projects.
Credits: 3.0
Prerequisites: CS121 OR DS115
Corequisites:
Program: BSCS
Course Code: CS220
Title: Parallel and High Performance Computing (Parallel HPC)
Description: The course examines topics including: parallel hardware architectures, distributed computing paradigms, parallelization strategies and basic parallel algorithmic techniques, parallel programming with OpenMP and MPI, HPC numerical libraries. Students should be able to demonstrate advanced knowledge related to contemporary methods in parallel and HP Computing. Students are required to draw upon investigative techniques related to this field in order to critically analyze and solve problems using advanced knowledge. Coursework will require students to develop faster codes that are highly optimized for modern multi-core processors and clusters. Three hours of instructor-led class time per week including discussions, lab work and problem sets.
Credits: 3.0
Prerequisites: CS211
Corequisites:
Program: BSCS
Course Code: CS221
Title: Cloud Computing
Description: The course covers general introductory concepts in the design and implementation of Cloud Computing, covering all the major branches such as cloud computing, cloud systems,
parallel processing in the cloud, distributed storage systems. The main principles underlying Cloud Computing will be investigated: processes, communication, naming, synchronization, consistency, fault tolerance, and security. The course gives some hands-on experience as well as some theoretical background. Moreover the course will go in deep of several technical issues in cloud systems, such as Amazon EC2/S3, and Hadoop (MapReduce framework). Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS211
Corequisites:
Program: BSCS
Course Code: CS226
Title: Math Modeling Applications
Description: This course introduces mathematical modeling and computational techniques for the simulation of a large variety of engineering and physical systems. The students will be able to apply real-world problem solving skills relating to modeling real-life scenarios from the natural sciences, business, social sciences, and finance. The applications for simulations are drawn from various fields and industries such as aerospace, mechanical, electrical, chemical and biological engineering, and materials science. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites: CS102
Corequisites:
Program: BSCS
Course Code: CS230
Title: Software Testing Fundamentals
Description: This course aims to cover the fundamentals of software testing, including important areas such as Fundamental Test Process, Test Design Techniques (white box, black box), Test Levels, Static Test Techniques, different types of Software Development Life Cycle models, Agile methodology (Scrum Framework), role of Continuous Delivery in modern world and role of Automated Testing in it. The practical part of the course will cover topics such as Test Automation strategies, Design Patterns used in Automation Testing and students will get hands-on experience with web app test automation Frameworks. Instructor-led discussion, along with reading, written, and practical assignments. Assessment via projects, hometasks, and exams.
Credits: 3.0
Prerequisites: CS120
Corequisites:
Program: BSCS
Course Code: CS231
Title: Quantum Computing
Description: The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. After developing the basics, this course delves into various implementation aspects of quantum computing and quantum information processing including the quantum fourier transform, period finding, Shor’s quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. Instructor-led discussion, along with reading, written, and practical assignments. Assessment via problem sets, projects and exams.
Credits: 3.0
Prerequisites: CS140 OR ENGS121
Corequisites:
Program: BSCS
Course Code: CS232
Title: Cybersecurity
Description: This course covers various security risks in Cyberspace from both offensive and defensive points of view, including subfields such as Web/Mobile Security, Network Security, and Cryptography. Students will develop skills of usage of various tools to be able to test the security of systems as well as build defense for those. Students will contribute to a team project in one of the following subfields (eg Web Security, Mobile Security, IoT security, Digital Forensics). Instructor-led discussion, along with reading, written, and practical assignments. Assessment via exams projects and hometasks.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS236
Title: Compiler Design
Description: An introduction to the basic phases of modern compilers and their design principles. Topics covered include CPU instruction, finite state machines, lexical scanning, parsing schemes, code generation and translation, comparison of modern programming languages. As part of the course, students build a working compiler for an object-oriented language. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS130
Corequisites:
Program: BSCS
Course Code: CS241
Title: Dynamical Systems
Description: The course covers topics including: concepts of continuous and discrete dynamical systems; orbits, fixed points and periodic orbits; 1D and 2D maps; stability of fixed and periodic points, sinks, sources and saddles; Lyapunov exponents; chaos; linear and nonlinear systems; periodic orbits and limit sets; chaotic attractors and fractals; maps of the circle, hyperbolic dynamical systems, horseshoe maps; symbolic dynamics, topological entropy. Students are required to solve problems in computational science utilizing concepts and methods from mathematical disciplines of mathematical modeling. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS245
Title: Bioinformatics
Description: This course is a brief introduction to molecular biology and investigates the main algorithms used in Bioinformatics. After a brief description of commonly used tools, algorithms, and databases in Bioinformatics, the course presents specific tasks that can be completed using combinations of the tools and Databases. The course then focuses on the algorithms behind the most successful tools, such as the local and global sequence alignment packages: BLAST, Smith-Waterman; and the underlying methods used in fragment assembly packages. The course will also be complemented by hands-on, computer lab sessions. Students will solve hands-on problems on HIV, BRCA1 gene, Thalassemia, FMF, etc. Forty-five hours of instructor-led class time.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS246
Title: Artificial Intelligence
Description: This course provides an introduction to the field of artificial intelligence, considering search as the fundamental technique for solving problems in AI. Various types of search (uninformed, informed, local) will be introduced and discussed, along with their application to solve problems in navigation, optimization, constraint satisfaction problems, planning, playing games, etc. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS211 OR DS115
Corequisites:
Program: BSCS
Course Code: CS251
Title: Machine Learning
Description: This course introduces the fundamental concepts and methods of machine learning, focusing on how computers can learn from data to uncover hidden insights without explicit programming. Topics include supervised learning, unsupervised learning, and best practices in machine learning, with numerous examples from real-world applications. The course includes instructor-led discussions and problem sets to reinforce the material.
Credits: 3.0
Prerequisites: CS104, CS108
Corequisites:
Program: BSCS
Course Code: CS252
Title: Data Science
Description: This course aims to introduce students to the world of data science. Students will gain the skills that are transforming entire industries from healthcare to internet marketing and beyond. In this course, students will gain a hands-on introduction to using R programming language for reproducible data analysis. Students will define the data science process, including data acquisition, data munging, exploratory data analysis, visualization and modeling real world data. The course will include using R and R packages tools for analysis of both structured and unstructured data sources, as well as writing reproducible data analysis reports with R Markdown and creating personalized interactive graphics applications. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS260
Title: Image Processing
Description: This course is a practical introduction to digital image processing. It covers the key methods and connects the mathematical foundations with programming implementation. Algorithmic descriptions of several approaches may include: image analysis, image representation and storage, image de-noising and restoration, compression techniques, two-dimensional discrete Fourier transform, spatial and frequency domain, linear and, optionally, nonlinear image filtering, edge detection, image segmentation and the basics of digital video processing. Instructor-led class time is supported by practical exercises and problem sets.
Credits: 3.0
Prerequisites: CS104, CS112, CS211 OR DS115, CS121 OR DS115
Corequisites:
Program: BSCS
Course Code: CS290
Title: Special Topics in Applied Computer Science
Description: This course explores topics in applied computer science with emphasis on current technologies and approaches. Topics to be announced prior to course registration. Instructor-led class time.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: BSCS
Course Code: CS296
Title: Capstone
Description: This course provides computer science majors the opportunity to develop the knowledge that they have obtained from across the curriculum. Students are encouraged to work in teams, and can choose either a theory or applied project. Students will select a topic from their respective tracks and work on the course-long project under the mentorship of the advising instructor. Students will discuss each other’s projects at scheduled weekly meetings led by the instructor. At the end of the course the projects will be presented and demonstrated orally and the project reports will be submitted in writing. Students are required to formulate and critically assess problems and sub-tasks including identifying sources and conducting independent research. Students should likewise be able to demonstrate expertise in core domains and in contemporary computing technologies. Students are required to produce technical documentation with references and demonstrate the capacity to discover and learn new material through independent research. Students are also required to draw upon critical thinking skills in a broad context and work as part of a team. Students choosing applied projects participate in the identification of a problem, develop a project proposal outlining an approach to the problem’s solution, implement the proposed solution, and test or evaluate the result. Students choosing a theory project conduct original research (e.g., develop a new algorithm) and evaluate its strengths and limitations. Regardless of the choice of project, students document their work in the form of written reports and oral presentations.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSCS
Course Code: CS299
Title: Independent Study
Description: nan
Credits: 1.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE111
Title: The Scientific Method and Critical Thinking
Description: Science and technology proficiency is indispensable for functioning in modern societies. We are overwhelmed with instant information in all sensory formats and we must be able to discriminate between facts and fallacies, while recognizing our own underlying biases. In this course, the student is introduced to the basic tenets of the scientific method, critical thinking and illustrated real world examples and case studies, with several general topics examined in depth. Such topics includes: pharmaceutical studies, computer performance claims, climate change, emerging technologies, marketing and advertisement, international relations, political and partisan hyperbole.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE112
Title: Mathematical Thinking
Description: Students will explore and develop quantitative analysis and numeracy skills, rooted in logic-based intuition, that are essential to succeed regardless of profession. In this course, students will expand critical thinking skills in the context of understanding and analyzing data and presenting findings/conclusions through the practical application of mathematical theories, principles and techniques rooted in algebra, calculus, probability and statistics in subjects such as demographics, finance, medicine, politics and economics. Through the use of advanced Microsoft Excel functions and formulas, students will expand problem-solving skills. Students will prepare oral and written reports that utilize concepts of the effective visual display of quantitative information to optimize how to summarize and explain mathematical solutions that emphasize clear and effective communication. Instructor-led discussion, along with reading, written, and practical assignments.Not open to CS, DS, ES students.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE120
Title: Introduction to the World of Programming
Description: This course covers the topics related to the role of computers in our
everyday life. Topics include high level overview of: history of computers, the architecture of
personal computers, mobile devices and other smart gadgets, the structure of internet and cloud,
search engines, data storages, data analytics tools, information management tools and
information security. Students will learn to write basic programs, implement basic algorithms,
collect and store data, browse the data in the web with smart search engines and which is very
important to understand the key areas of information security. This course is designed for
students with no prior background of computer science. Instructor-led discussion, along with
reading, and practical assignments. Not open to CS, DS, ES students.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE131
Title: Industrial Technologies
Description: The course will explore several industrial technologies, with an aim for students to familiarize themselves with both traditional as well as modern innovative business practices. Examples of these industrial technologies may include metallurgy, construction industry, chemical production, lighting technologies, semiconductor manufacturing and other cutting edge “technologies of tomorrow.” While no prior experience or knowledge of these technologies is expected, students will gain firsthand exposure to real production processes and appreciation of product lifecycles including environmental and technological. Instructor led lecture and discussions; assessment may include problem sets, essays, exams, and projects.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE141
Title: Understanding Data
Description: In this course students will explore the fundamental concepts of data, starting with the basics of descriptive statistics and ending with data visualization. Students will learn how data is used and misused, discover patterns in data, and present data to make actionable decisions. The models and methods may be applied in different fields such as business, social sciences, health care, sports, etc. While software will be used, no prior knowledge in programming or statistics is necessary for the course. [not open to DS students]
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE150
Title: Cybersecurity and Society
Description: “Cybersecurity and Society” is a comprehensive interdisciplinary course focusing on the link between cyberspace and the social,
political, and legal systems in national and international governance. Before introducing the cyberspace as a domain in policy, governance, and national security the course delves into the fundamental technical aspects of cybersecurity, covering the principles of network security, data encryption, threat detection, and risk assessment. Students will gain a robust technical understanding crucial to navigating the complex cybersecurity landscape as well as knowledge concerning the role of cybersecurity in modern politics and warfare, the main approaches to online governance and regulation, the intersection of datafication, data privacy and human rights. To cultivate these skills, the course utilities multimedia content, and offers practical exercises and case studies in cybersecurity policy-making. Students will gain comprehensive insights into how cybersecurity policies and procedures intertwine with larger societal constructs, equipping them with a flexible toolkit to influence the future of cyberspace and national security.
Not open to CS students.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE151
Title: Introduction to Energy Sources
Description: Energy drives the human civilization, and any economic growth or poverty alleviation directly involves use of energy resources. This course serves as an introduction to various sources of energy and the mechanisms to harness and convert them to more useful types of energy. Fossil fueled, solar, hydro and nuclear sources and some of their effects on the environment and safety issues will be discussed. This course fulfills general education requirements of the American University of Armenia. There are no prerequisites for this course beyond basic mathematical skills. Three hours of instructor-led class time per week.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE162
Title: Introduction to Bioscience
Description: This course introduces students to important concepts, techniques and applications of bioscience, and explores its impact on research, business and society. Students will study basic concepts of molecular and cellular biology, biochemistry, molecular genetics, computational biology and biotechnology. Some important applications of molecular and cellular biology in medicine and industry – such as molecular diagnostics of diseases, stem cell and transplantation, drug design and genetically modified foods – will be introduced. Students will also discuss the political, ethical, and legal issues accompanying these topics and their current and future impact on society. Three hours of instructorled class time per week.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE170
Title: Chemistry in Everyday Life
Description: The course highlights and discusses the practical chemical world of human beings and the chemical nature of everyday processes. The role of chemistry in necessities of daily life such as the chemistry of life, agriculture, food, housing, healthcare, clothing, household goods (e.g., toys, furniture, etc.), transport and communications will be discussed. In addition the course will introduce various applications of chemistry in the area of arts, crime and law enforcement, consumer products, cosmetics and warfare. As a science-based, quantitative course, the course will teach students the methods of scientific inquiry, including experimental design and chemical analytical methods, data generation and analysis, and presentation of the final results. Instructor-led discussion, along with reading, written, and practical assignments. Not open to ESS, ES majors or to DS majors in the Bioinformatics track
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE171
Title: Conceptual Physics
Description: This course will explore the basic concepts in physics and physical processes. The conceptual viewpoint taken in the course will focus more on the physical description of the processes and phenomena rather than the detailed mathematical equations that govern them. The course will cover topics in mechanics of moving bodies, heat transfer, propagation of sound, properties of light, electricity and magnetism with special emphasis on everyday experience and practical illustrations taken from real life, e.g. art, music, sports, the home. For each of the processes covered in the course, a brief historical perspective will be given, followed by a description of its physical principles, and finally the basic equations that describe it mathematically. Students will be exposed to real-life applications of the theories discussed in the classroom. Three hours of instructor-led class time per week. Not open to CS, DS, ES students.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE181
Title: Creativity and Technological Innovation
Description: This course introduces students to creativity and its elements, the creative mind and thinking, techniques, concepts and applications leading to technological innovations. Lectures will provide examples of creative thinking and technological innovations from real life creators and technology innovators whose work is well known. Students will work in groups. Each group will create a technological project attempting to solve a real life need based on the knowledge gained and discussed during the semester. Students will be introduced to various problem-solving techniques. Upon completion of this course, students will be able to think creatively and they will be familiar with the process of technological innovation and invention. Three hours of instruction-led class time per week.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE190
Title: Engineering for non-Engineers
Description: This course aims to give students an insight about basic principles of engineering and its different sub-disciplines. The course will explore the role engineering has played in shaping society today through its various advancements in different fields, e.g. manufacturing, the energy sector, urban development and materials engineering. Student evaluation will be based on individual or group projects, research essays and written examinations. Instructor-led class time. Not open to ES students.
Credits: 3.0
Prerequisites:
Corequisites:
Program: General Education
Course Code: CSE210
Title: Historical Development of Mathematical Ideas
Description: This course will provide an exploration into the history, birth and development of mathematical ideas, problems and people behind them. A variety of topics will be covered, such as: infinity and paradoxes; numbers and set theory; algebraic equations and algebra; limits and calculus; shapes, symmetry and geometry; gambling, uncertainty and probability; physics and differential equations; choice and game theory; data analysis and statistics. Students are required to complete problem sets and quizzes, and to complete a group project, as well to conduct collaborative research. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE220
Title: Tomorrow’s Technologies
Description: What is technology? Why it is so crucial to constantly improve or develop new theories based on observation and apply scientific methods toward the creation of innovative products. How are scientific theories developed? Regarding the creation process, what is the role of engineers, of society and of government? In this course students will deepen their understanding of design and development of cutting-edge interdisciplinary technologies, such as quantum computers, organic displays, and artificially-grown materials. Applications of these new technologies are poised to revolutionize industries such as health care, energy use, and consumer goods. Instructor-led discussion, along with reading, written, and practical assignments, projects, and exams.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE221
Title: Nanotechnology: Science and Application
Description: Nanotechnology: Science and Application is a multidisciplinary course which presents an overview of the main aspects of the emerging field of Nanotechnology. Students will become acquainted with a set of disciplines which form the scientific basis upon which Nanotechnology research and applications are developing. Students will also gain familiarity with examples of nanotechnology applications in evolved or emerging industries, such as agriculture, consumer goods, aerospace, electronics, etc. The course covers basic principles from the fields of physics, chemistry, and engineering to enable students to implement a synthesizing project. There are no prerequisites other than high-school level mathematics and physical/natural science. Instructor led lecture and discussions; assessment may include problem sets, essays, exams, and projects.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE230
Title: Music Technology
Description: The course investigates properties of the rudimentary element of music – sound, by exploring musical acoustics, psychoacoustics, analog and digital audio technologies such as electroacoustics and innovative designs of audio transducers (microphones, speakers, headsets, earbuds, binaural systems), acoustical properties for musical reproduction spaces (from recording studios to concert halls and immersive audio setups), in addition to presenting digital audio workstations for audio manipulation and multimedia content creation (including commercials, films, gaming, VR, etc). Instructor led discussions and lectures, assessment by projects, homework, and exams.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE241
Title: Data Mining
Description: The goal of the course is to present the basic concepts of data analytics, starting from the basics of descriptive statistics and ending with applications of data mining. Students will learn how statistics is used to model uncertainty, discover patterns in data and make actionable decisions. Basic methods of statistical inference and predictive modeling will be covered. The models and methods will be applied in different fields such as business, social sciences, health care, sports, etc. We will use analytical software to engage in statistical calculations. No prior knowledge in programming or experience with programming is necessary for the course. Not open to DS students.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE263
Title: Human Physiology
Description: This course aims to build knowledge regarding the interrelationship between the nine organ systems responsible for the healthy functioning of the human body. Analysis will encompass from cells and tissues to the entire organism, underpinning the role of major structures supporting physiological processes. Important diseases will be discussed, including their causes and consequences as examples of disturbed homeostasis and dysfunction of human body systems. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE264
Title: The Human Brain
Description: The course will cover an introduction to the brain anatomy and the cellular function of neurons, synapses and neurotransmitters. The work of human brain in health and in some disorders as well as the mechanisms of vision, learning, memory, feelings and emotions will be discussed.Applications of the knowledge may be relevant in a variety of realms including for marketing specialists, user interface and software developers as well as public policy makers and educators. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE265
Title: Genetics
Description: An introductory course in genetics and their impact on society and life. Students will explore DNA, genes and chromosomes, classical Mendelian concepts, genetics in the real world, the ethics associated with advancing gene technology and applications, genetic diseases and genomics.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE270
Title: Sports Analytics
Description: Professional sport organizations are using analytics to make better decisions on team formation, playing strategy etc. Enthusiasts use analytics to predict the outcome of a sporting event and to try to quantify reasons that lead to victory. The course will examine how different statistical and data analytics methods can be used to analyze game-day (in-play) sports data and for pre- and post-game sports performance modelling. We will focus on several team games, e.g. soccer, basketball, American football and baseball. The course will use a statistical programming language such as R and assessment may include problem sets, individual or group projects and written examinations.
DS students can take the course as a free elective, not as a general education course.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE271
Title: Number Statistics and the Environment
Description: The course is a practical introduction to general quantitative and statistical techniques that can be applied to geography and environmental studies. Students will learn techniques to verify quality of data, analyzing trends and tendencies, and estimating probability of outcomes. The course will also cover topics such as proposing and verifying hypotheses using numbers and statistical analysis. Each topic will begin with an introduction to a numerical or statistical concept followed by the application of that concept on a real world environmental problem. As the course progresses, students will also be introduced to software that utilizes these concepts. Problem sets and written examinations. Instructor led class time.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE281
Title: Design Thinking
Description: Design Thinking is a way of approaching problems, a method used by designers for ideation and development that has a wide range of applications.
The tools and methods in this course include a set of creative strategies and borrow from a variety of disciplines, including ethnography, computer science, psychology and organizational learning, all to drive innovation. This course is focused on the tools and strategies needed for reframing challenges in human-centric ways, encouraging the designer to focus on what’s most important for clients, users & customers. Students will engage in the design process, create prototypes, test ideas, plan and conduct effective design research, make and use storyboards to communicate design concepts. Instructor-led discussion, along with readings, written assignments, and team-projects.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE285
Title: How Things Work
Description: This course introduces students to detailed explanations behind the theory, function, and operation of selected technologies, answering the question, How does that work? This is a course in the physical and technological innovations in everyday life employing a minimum of mathematics. It explores the principles of automobiles, propulsion, digital media, cellular technologies, cyber security, nuclear and solar power generation, computer systems, etc. In-class demonstrations will aid in demystifying many topics. Lectures will look inside products from our daily lives to see what scientific principles make them work, focusing on their principles of operation, histories and relationships to one another. Students will work individually, and additionally, present to the class as a group on an emerging technology. The course will be split into three themes: The Digital World, Power and Energy, and Daily Motion. Three hours of instructor-led class time per week.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE290
Title: Start-up Culture
Description: This course provides practical proven tools for transforming an idea into a product or service that creates value for people. As students acquire these tools, they will learn how to differentiate between good and bad ideas, how to build a winning strategy, how to shape a unique value proposition, design a business model, compare the innovation to existing solutions, build flexibility into a plan and determine when best to quit. This course guides students through the process of actively validating ideas in the market. Students are encouraged to identify and communicate good opportunities and to create and capture value from those. Students will receive feedback that systematically tests different parts of their business idea and develop confidence in pitching ideas to investors and customers. Instructor led discussions and lectures; assessment through projects, exams, assignments, and presentations.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE291
Title: Introduction to Product Design
Description: An introduction to 3D design techniques and graphics communication tools necessary for a product design. Students learn 3D modeling, assembling, mechanism design, and simulation tools via Parametric Technology Corporation (PTC) company’s online tutorials and demonstrations. Through number of lectures they learn also basic product design communication tools – drawing standards, units, projection views, dimensioning, sections, etc. The knowledge acquired during the course will help students transform their ideas to Computer-Aided Design 3D models and drawings. Also, they will be prepared to apply these powerful design tools in further more advanced courses and their work practice. The evaluation will be done through PTC Precision Learning portal self-assessment questions, home assignments and product design project.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: General Education
Course Code: CSE292
Title: Building a Learning Organization
Description: This course will introduce the underlying principles that individuals, teams and organizations can acknowledge and implement in order to achieve continuous and sustainable development. The course will use Systems Thinking and analysis of cases studies from companies worldwide to provide an understanding of events that develop or destroy companies, markets, teams and individuals. By appreciating the underlying processes that can cause success or failure, students will be equipped with tools and methods to analyze interdependencies between people, their decisions and the corresponding consequences. Instructor led lecture and discussions; assessment may include problem sets, essays, exams, and projects.
Credits: 3.0
Prerequisites: Sophomore level required
Corequisites:
Program: BSDS
Course Code: DS110
Title: Statistics 2
Description: The course covers nonlinear regression models including logistic regression, regression models with categorical independent variables, interaction terms and non-linear transformations of the predictors, factorial experiments, introduction to nonparametric statistics and nonparametric hypothesis testing, Bayesian statistics: Bayesian Priors, Posteriors, and Estimators, Bayesian hypothesis testing. Instructor-led discussions and problem sets with assessment including exams, projects, and problem sets.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS115
Title: Data Structures/Algorithms in Data Science
Description: The data structures part of the course will give students the knowledge to implement their algorithms using procedural and functional programming techniques and their associated data structures, including lists, vectors, data frames, dictionaries, trees, and graphs. The part of the courses dedicated to algorithms will help students to develop the skill set to understand the problem, break it into manageable pieces, assess alternative problem-solving strategies and arrive at an algorithm that efficiently solves the given problem. Class examples and homework will help students to apply the knowledge in data science domain.
Credits: 4.0
Prerequisites: CS111, DS120
Corequisites:
Program: BSDS
Course Code: DS116
Title: Data Visualization
Description: The course is about the art and science of turning raw data into readable and useful visuals. The students will learn how to choose appropriate visualizations for numeric and categorical variables, the principles of visualization for univariate and multivariate data, visualization of spatial data, text data, etc. The course also provides the foundation for grammar of graphics. The second part of the course will focus on developing visual dashboards. Assessment by problem sets, projects, and exams. Instructor led discussions.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS120
Title: Programming for Data Science
Description: The course covers fundamentals of programming for data science such as classes, methods, procedures, control structures, functions, arrays, strings, scoping. The course will emphasize the use of programming essentials for data science-related tasks, such as working with dataframes, numeric calculations with vectors and lists, etc. The course will make use of programming languages widely used in data science such as Python and R. Three hours of instructor led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS110
Corequisites:
Program: BSDS
Course Code: DS150
Title: Physics and Chemistry in Life Science
Description: Introductory course to the foundations of chemistry and physics. Topics in chemistry include: atoms and molecules, chemical reactions, chemical solutions, chemical bonding, etc. The course provides an overview of topics in physics that are of particular importance to the life sciences and bioinformatics, including mechanics, electricity and magnetism, heat, nuclear physics, fluids, and waves, etc. Instructor led discussions. Assessment by problems sets, projects and exams.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS151
Title: Cell and Molecular Biology
Description: This course is aimed to provide understanding of the fundamental processes of cellular functions going on in prokaryotic and eukaryotic cells. The first part of the course focuses on the macro level with an exploration of basic cell characteristics, cellular membranes, cellular respiration and cell interaction with the environment. The second part of the course focuses on genetics with a look at chromosomes, genes, gene expression, how cells accomplish DNA replication, repair errors that can result in DNA, how cells reproduce, how cells communicate.The last part of the course explores the relationship between cancer and the immune system at the cellular level. In each topic the appropriate techniques in cell and molecular biology will be discussed
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS205
Title: Database Systems
Description: This course focuses on the design and system issues related to distributed database systems. Students will learn the usage of different design strategies for distributed databases, and they will study query processing techniques and algorithms as well as transaction management and concurrency control concepts used in such systems. Design and implementation issues related to multidatabase systems are discussed as well. The course will cover graph databases, relational and non-relational database structures. Instructor led discussion. Assessment by problem sets, exams and projects.
Credits: 3.0
Prerequisites: CS121 OR DS115
Corequisites:
Program: BSDS
Course Code: DS206
Title: Business Intelligence
Description: This course provides an introduction to the concepts of business intelligence. It explores the essential components of BI project lifecycle: project planning, BI tool selection, data modelling, ETL (extract, transform, load) design, BI application/dashboard design and deployment. The course approaches BI from both managerial and technical viewpoints: the managerial perspective helps to understand how BI can support the organization’s decision-making processes, while the technical perspective explores the tools and techniques for developing for BI solutions. Learning is supported by individual and group projects, as well as assignments and case studies. Instructor-led discussions, with assessment by problem sets exams and projects.
Credits: 3.0
Prerequisites: DS205
Corequisites:
Program: BSDS
Course Code: DS207
Title: Time Series Forecasting
Description: This course introduces the fundamental techniques for time series forecasting and analysis. The topics will include regression analysis, ARMA/ARIMA modelling, (G)ARCH modeling, VAR models, along with diagnostics and forecasting, and more. Mathematical formulation and assumptions underlying these statistical models, the consequence and the potential solutions when one or more of these assumptions are violated, are emphasized throughout. Students who successfully complete this course will be able to choose from available techniques to handle the real-world data, understand the trade-offs between models, and capture key patterns contained in the data. Instructor-led discussions, with assessment by problem sets exams and projects.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS209
Title: Spatial Data Science
Description: The course is an intensive introduction to spatial data science, covering topics from basic spatial data types, GIS, and coordinate systems to more advanced geostatistical, spatial machine learning, and spatial optimization methods. Having a strong applied focus, students will explore spatial data and use the methods and techniques to formulate and tackle complex real-world spatial data science problems. Students are expected to complete regular reading and coding assignments. Home tasks and assessment will include problem sets, discussion of case studies, and implementation of state-of-the-art spatial data analysis methods and algorithms from journal articles.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS211
Title: Introduction to Bioinformatics
Description: In this course, students learn fundamental concepts, methods, databases and algorithms in bioinformatics. The course covers specific tasks that can be completed using databases and algorithms. Three hours of instructor-led class time per week.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS213
Title: Computational Biology
Description: This course focuses on the analysis of NGS and -omics datasets to study complex biological problems. The course will expand on processing the data produced by next generation sequencing (NGS) technologies, e.g. read mapping, variant calling for DNA-seq datasets, gene expression estimation from RNA-seq datasets and peak calling from ChIP-seq datasets. Case studies are explored to identify disease-linked genetic features via genome wide association studies; to identify differentially expressed genes and differentially regulated biological processes in a disease; to perform genome assembly and species annotation in metagenomic datasets; to identify evolutionary relationship between species via phylogenetic studies; to apply machine learning for single cell analysis and annotation; and to integrate multiple sources of -omics data into a single framework.
Credits: 3.0
Prerequisites: CS251, DS211
Corequisites:
Program: BSDS
Course Code: DS215
Title: Networks and System Biology
Description: This course explores the nature and properties of protein-protein interaction (PPI) networks and the functions of specialized sub-networks or biological pathways. The course will introduce a short overview of small world properties of biological networks, network patterns underlying regulatory feedback loops with simple graph theory algorithms. The students will also apply simplified rule-based modeling to investigate activation or repression of biological pathways. Examples drawn from case studies will be used to elucidate differential regulation of biological pathways in a disease. Tools and solutions for PPI and pathway annotation as well as machine learning based text mining will also be discussed.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS216
Title: Cheminformatics
Description: Cheminformatics is a field of information technology that links together chemistry and computer science. One of the major applications of cheminformatics is in drug discovery, where cheminformatics is used to store and analyze chemical data, as well as apply machine learning techniques to predict chemical properties or design new chemical motifs. This course includes topics such as molecular representation, chemical data manipulation, molecular property prediction. It also includes high level discussions on state-of-the-art methods of applying machine learning techniques for drug discovery.
Credits: 3.0
Prerequisites: CS108, DS115, DS150
Corequisites:
Program: BSDS
Course Code: DS217
Title: Biostatistics
Description: The course covers development and application of statistical methods to a wide range of topics in biology. Examples will be drawn from subfields such as pharmacology, drug design, genetics, and molecular biology. The course is comprised of instructor-led lecture notes and practical exercises.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS219
Title: Causal Inference
Description: This course provides students with an introductory theory of causal inference for observational studies where randomized experiments are not possible. Topics include potential outcomes framework (matching and stratification, propensity weighting), causal graphical models, etc. Students will develop skills in implementing models using python and R to solve problems from various disciplines.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS221
Title: Urban Data Science
Description: This course provides a comprehensive introduction and overview of data science methods for informing urban planning, city management, and smart city solutions. It will cover the fundamentals of urban economic theory – mathematically exploring how cities form, grow, and function; urban mobility and transport planning and their relationship to land use; and will focus on applying spatial statistics, machine learning, spatial optimization and network theory to tackle real-world problems facing urban planners and policy makers. Such problems include to determining where in a city a new transit line should be introduced, how to plan a city to achieve equitable access to urban facilities for all citizens, how to optimally route snow cleaning and waste collection vehicles, how to predict urban mobility, and how to make use of data visualisation to build urban monitoring tools for decision makers. Home tasks and assessment include regular reading and coding assignments, as well as discuss case studies and the latest methods and algorithms.
Credits: 3.0
Prerequisites: CS108, ENGS103
Corequisites:
Program: BSDS
Course Code: DS223
Title: Marketing Analytics
Description: The course concentrates on data science tools and methods that help to transform customer data into actionable findings. Topics include estimation of customer lifetime value, measuring marketing campaign ROI, new product development, advertising response models, etc. The students will learn how to define a business problem and chose appropriate data science method for it. Students are expected to have basic programming skills. Assessment by problem sets, projects, and examinations.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS225
Title: Applications of Machine Learning in Science
Description: Students will be introduced to problems from natural and applied sciences that have been solved using machine learning (ML) methods such as deep neural networks and reinforcement learning. In the first part of the course, we will have a journal club-style study of recently published papers. For example, we will study how the protein folding problem has been largely solved using transformer-based deep learning algorithms, and we will learn about novel protein design using diffusion models. In the second part of the course, students will conduct group projects where they will apply ML algorithms to address problems from natural or applied sciences.
Credits: 3.0
Prerequisites: CS251
Corequisites:
Program: BSDS
Course Code: DS226
Title: Bayesian Statistics
Description: The course is an introduction to the theory of Bayesian Statistical Inference and Data Analysis. Both refer to practical inferential methods that use probability models for both observable and unobservable quantities. The flexibility and generality of these methods allow them to address complex real-life problems that are not amenable to other techniques. This course will also provide a pragmatic introduction to powerful applications of those methods. Topics include: the basics of Bayesian inference for single and multiparameter models, regression, hierarchical models, model checking, approximation of a posterior distribution by iterative and non-iterative sampling methods.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: BSDS
Course Code: DS227
Title: Business Analytics for Data Science
Description: This course will focus on business understanding and problem framing. This includes analysis of previous findings and identifying stakeholders’ challenges, understanding the components of analytics framework to compete on analytics; developing a data strategy for defining key performance metrics; introducing big data concepts and technological infrastructure for processing information; discussing innovative business models, appropriate analytical tools and necessary leadership role to implement analytics initiatives and prioritize them for budgeting, efficient resource allocation, effective creation of shared values and sustainable performance growth in a business domain.
Credits: 3.0
Prerequisites: BUS101, DS205
Corequisites:
Program: BSDS
Course Code: DS228
Title: Product Management
Description: The purpose of this course is to teach the students what product management is, who are product managers and what kind of skill set one needs to have to be successful at this role. We will cover the whole product management and product development lifecycle starting from ideation, idea validation, MVP experiments and implementation, success metrics and KPI setup and follow up. Students will learn to create products or features from A to Z. Students are expected to have programming/coding skills to succeed in the course. There will be no coding sessions in this course.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS229
Title: Machine Learning Operations (MLOps)
Description: Machine learning operations (MLOps) is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. This course will familiarize students with the key aspects of end-to-end machine learning (ML) projects and ML-specific technical depth. The main goal of the course is to teach students the core concepts, methodologies and challenges in deployment and monitoring of Machine Learning systems with a primary focus on testing and monitoring of such systems.
Credits: 3.0
Prerequisites: CS251
Corequisites:
Program: BSDS
Course Code: DS231
Title: Computer Vision
Description: This course provides a comprehensive introduction to the field of computer vision, focusing on the fundamental techniques and concepts used to extract meaningful information from digital images and videos. Topics include: image formation and representation, color models, image processing techniques, edge detection, feature extraction, image segmentation, object recognition, motion analysis, 3D scene reconstruction, and deep learning for computer vision. Students will learn to apply various computer vision algorithms and techniques to real-world problems using popular programming languages and libraries. Through hands-on projects, students will develop skills in image analysis, pattern recognition, and machine learning techniques relevant to computer vision applications.
Credits: 3.0
Prerequisites: CS251
Corequisites:
Program: BSDS
Course Code: DS232
Title: Reinforcement Learning
Description: Reinforcement Learning (RL) refers to a collection of machine learning techniques that solve sequential decision-making problems using a process of trial and error. It is a core area of research in artificial intelligence and machine learning. This course covers the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. Students also will be introduced to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Students will cover model-based and model-free (such as Policy Gradient, and Q-learning) algorithms.
Credits: 3.0
Prerequisites: CS251
Corequisites:
Program: BSDS
Course Code: DS233
Title: Natural Language Processing
Description: The course goal is to provide a solid background in theory and techniques of Natural Language Processing for tasks such as understanding and generating natural language, machine translation, text classification, named entity recognition, topic modeling, etc. The course will focus on solving applied problems together with providing solid theoretical background.
Credits: 3.0
Prerequisites: CS251
Corequisites:
Program: BSDS
Course Code: DS235
Title: Generative AI
Description: The integration of a course on Generative AI into a data science program is a strategic move to ensure that students are well-versed in the latest advancements in artificial intelligence. Generative models represent a significant leap forward in AI’s capability to create realistic text, images, and other types of data, opening up a plethora of possibilities across various industries, from healthcare and entertainment to manufacturing and beyond.
Equipping students with a comprehensive understanding of these models will prepare them for cutting-edge roles and innovative projects in their future careers. They will learn not only the technical skills to implement and optimize these models but also the critical thinking required to navigate the ethical considerations associated with them.
Credits: 3.0
Prerequisites: CS251
Corequisites:
Program: BSDS
Course Code: DS244
Title: Biomedical Imaging and Cell Staining
Description: Students will work in groups to learn the core principles of cell and tissue staining, as well as the application of advanced biomedical imaging modalities. The latter will encompass practical instruction in capturing images through confocal microscopy, hyperspectral imaging, and atomic force microscopy. Following this, students will employ software teachniques to extract quantitative data from the images they have acquired.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS250
Title: Managerial Accounting and Analysis
Description: The course covers business concepts and methods used to report managerial performance information to internal users and managers to assist in making sound business decisions in managing the firm. Students will also learn how to analyse the efficiency of the firm’s management using financial data and how these data can be transformed into data science problems/projects. Assessment by projects, problem sets, and exams. Instructor led discussions.
Credits: 3.0
Prerequisites: BUS101
Corequisites:
Program: BSDS
Course Code: DS290
Title: Special Topics in Data Science
Description: Students will apply their data science knowledge to solve a real-world problem. The specific topic area and application will be defined in the given term of implementation. Course assessment will include instructor-led projects. The number of credits is variable.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSDS
Course Code: DS299
Title: Capstone
Description: Students will use their accumulative knowledge to solve real-world problems with real-world data. During the project, students will follow the entire process of solving a real-world data science project: collecting and processing actual data, applying suitable and appropriate analytic methods to the problem, presenting results and findings. Students will be provided with a list of projects to choose from; however they are also encouraged to introduce their own projects. To conclude, students will do presentation of their findings and will submit reproducible report (codes and used datasets).
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS104
Title: Probability and Statistics
Description: The topics covered in this introductory course include: axioms of probability; conditional probability, independence; combinatorial analysis; random variables and distributions; expectation, variance, covariance; transformation of random variables; limit theorems, the law of large numbers, the central limit theorem; Markov chains; applications; statistical estimation; correlation, regression; hypothesis testing, maximum likelihood estimation, Bayesian updating; applications. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: CS104
Corequisites:
Program: BSES
Course Code: ENGS110
Title: Introduction to Programming
Description: This course covers the fundamental elements of imperative programming languages (variables, assignments, conditional statements, loops, procedures, pointers, recursion), simple data structures (lists, trees) and fundamental algorithms (searching, sorting). Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS121
Title: Mechanics
Description: This course introduces students to classical mechanics. Topics include: space and time; straight-line kinematics; motion in a plane; forces and static equilibrium; Newton’s laws; particle dynamics, with force and conservation of momentum; angular motion and conservation of angular momentum; universal gravitation and planetary motion; collisions and conservation laws; work, potential energy and conservation of energy; vibrational motion; conservative forces; inertial forces and non-inertial frames; central force motions; rigid bodies and rotational dynamics. Instructor-led class time including discussions and problem sets.
Credits: 3.0
Prerequisites: CS101 or equivalent
Corequisites:
Program: BSES
Course Code: ENGS122
Title: Mechanics Lab
Description: Hands-on laboratory course to accompany Mechanics. Students will conduct experiments in support of the topics covered in Mechanics.
Credits: 1.0
Prerequisites:
Corequisites: ENGS121
Program: BSES
Course Code: ENGS123
Title: Electricity and Magnetism
Description: This course introduces students to topics related to electricity and magnetism, including Coulomb’s law, electric and magnetic fields, capacitance, electrical current and resistance, electromagnetic induction, light, waves, quantum physics, solid state physics, and semiconductors. Instructor-led class time including discussions and problem sets.
Credits: 3.0
Prerequisites: CS101 or equivalent
Corequisites:
Program: BSES
Course Code: ENGS124
Title: Electricity and Magnetism Lab
Description: Hands-on laboratory course to accompany Electricity and Magnetism. Students will conduct experiments in support of the topics covered in Electricity and Magnetism.
Credits: 1.0
Prerequisites:
Corequisites: ENGS123
Program: BSES
Course Code: ENGS131
Title: Chemistry
Description: This course introduces students to principles of chemistry. Topics include atomic theory, periodic properties, stoichiometry, nomenclature, bonding, physical properties of states of matter, solutions, kinetics, equilibrium, acid-base reactions, metathesis reactions, redox reactions, thermodynamics, electrochemistry, and chemical properties of selected classes of compounds. Instructor-led class time including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS132
Title: Chemistry Lab
Description: Hands-on laboratory course to accompany Chemistry. Students will conduct experiments in support of the topics covered in Chemistry.
Credits: 1.0
Prerequisites:
Corequisites: ENGS131
Program: BSES
Course Code: ENGS141
Title: Engineering Statics
Description: This course introduces students to fundamental engineering principles such as forces, moments, couples, resultants of force systems, equilibrium analysis and free-body diagrams, analysis of forces acting on members of trusses, frames, shear-force and bending-moment distributions, Coulomb friction, centroids and center of mass, and applications of statics in design. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: ENGS121
Corequisites:
Program: BSES
Course Code: ENGS142
Title: Engineering Dynamics
Description: This course engages students in formulating and solving problems that involve forces that act on bodies which are moving. Topics include kinematics of particles and rigid bodies, equations of motion, work-energy methods, and impulse and momentum, translating and rotating coordinate systems. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: CS104
Corequisites:
Program: BSES
Course Code: ENGS151
Title: Circuits
Description: Introductory course in fundamental electrical circuit theory as well as analog and digital signal processing methods currently used to solve a variety of engineering design problems. Circuit and system simulation analysis tools are introduced and emphasized. Topics include basic concepts of AC/DC and digital electrical circuits, power electronics, linear circuit simulation and analysis, op-amp circuits, transducers, feedback, circuit equivalents and system models, first order transients, the description of sinusoidal signals and system response, analog/digital conversion, basic digital logic gates and combinatorial circuits. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: ENGS123
Corequisites:
Program: BSES
Course Code: ENGS152
Title: Circuits Lab
Description: Hands-on laboratory course to reinforce concepts covered as well as provide system-level understanding. Students will conduct experiments in support of the topics covered in Circuits.
Credits: 1.0
Prerequisites:
Corequisites: ENGS151
Program: BSES
Course Code: ENGS161
Title: Introduction to Aerodynamics
Description: This is an introductory course in Aerodynamics which covers advanced incompressible potential flow theory, followed by incompressible flow around thin airfoils (2D) and wings (3D). Subsequently, 1-D and 2-D compressible gas dynamics is covered, including oblique shocks and Prandtl-Meyer expansions, as well as linearized subsonic and supersonic potential flows. The course also has a brief overview of aerospace propulsion systems as it pertains to air-breathing engines. The course incorporates computational fluid dynamics and other numerical methods when applicable.
Credits: 3.0
Prerequisites: CS102, ENGS141
Corequisites:
Program: BSES
Course Code: ENGS176
Title: Environmental Engineering
Description: nan
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS181
Title: Introduction to Materials Science
Description: In this introductory course, the students will gain fundamental knowledge about materials, their types, structures and properties. Material structure ranging from atomic scale to macroscopic scale will be discussed. The relationship between the material’s structure and the properties will be demonstrated. Properties like mechanical, optical, electrical etc. will be discussed. Most of the concepts will be discussed using real world examples, demonstrating capabilities and limitations of materials in our everyday lives. This knowledge will then be applied to material selection decisions.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS211
Title: Numerical Methods
Description: This course covers fundamentals of numerical methods in engineering. Topics include floating-point computation, systems of linear equations, approximation of functions and integrals, and numerical analysis and solutions of ordinary differential equations. Instructor-led class time including computational platforms, problem sets and discussions.
Credits: 3.0
Prerequisites: CS101 or equivalent, CS104 or equivalent
Corequisites:
Program: BSES
Course Code: ENGS230
Title: Introduction to Quantum Computing
Description: The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. After developing the basics, this course delves into various implementation aspects of quantum computing and quantum information processing including the quantum fourier transform, period finding, Shor’s quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. Instructor-led discussion, along with reading, written, and practical assignments. Assessment via problem sets, projects and exams.
Credits: 3.0
Prerequisites: CS140 OR ENGS121
Corequisites:
Program: BSES
Course Code: ENGS241
Title: Computer-Aided Design
Description: Fundamentals of part design; computer-aided design tools and data structures; geometric modeling; transformations; CAD/CAM data exchange; mechanical assembly. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: CS104
Corequisites:
Program: BSES
Course Code: ENGS242
Title: Computer-Aided Manufacturing
Description: he course introduces Computer-Aided Manufacturing technologies in Numerical Control and Reverse Engineering. Some of the topics from these disciplines covered during the course are 3D scanning/coordinate measuring, 3D printing, vacuum forming, composites, as well as NC fundamentals, manual NC programming, and computer-aided part programming. Students are also acquainted with topics on material removal processes such as metal cutting fundamentals, design for manufacturability, design for machining, and process engineering.
Credits: 3.0
Prerequisites: ENGS241
Corequisites:
Program: BSES
Course Code: ENGS245
Title: Thermodynamics
Description: This course introduces the basic principles of thermodynamics. Laws of thermodynamics will be discussed and concepts such as energy, work, power, entropy etc. are introduced. Various thermodynamic cycles along with engineering examples will be discussed.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS246
Title: Heat Transfer
Description: nan
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS248
Title: Introduction to Fluid Mechanics
Description: This course covers inviscid and viscous incompressible fluid dynamics. Fundamental topics presented include: fluid kinematics and deformation; integral conservation laws of mass, momentum and energy for finite systems and control volumes; differential conservation laws of mass, momentum and energy; the Navier-Stokes equations. Applications will be considered from the following topics: hydrostatics; Bernoulli equation; the stream function and the velocity potential; incompressible, inviscid, irrotational (potential) flows; incompressible boundary layer flows; viscous incompressible steady internal and external flows; and dimensional analysis.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS251
Title: Embedded Systems
Description: This course introduces students to the unique computing and design challenges posed by embedded systems. Students will solve real-world design problems using small-scale and resource-constrained platforms. Examples will be drawn from combined hardware and software systems and basic interactions between embedded computers and the physical world. Emphasis is placed on interfacing embedded processors with common sensors and devices (e.g. temperature sensors, keypads, LCD display, SPI ports, pulse width modulated motor controller outputs) while developing the skills needed to use embedded processors in systems design. Instructor-led class time including problem sets, discussion, as well as experimentation using hardware/software equipment.
Credits: 3.0
Prerequisites: ENGS151
Corequisites: CS130
Program: BSES
Course Code: ENGS252
Title: Signals and Systems
Description: This course develops further understanding of principles of electrical and mechanical systems. Topics include representations of discrete-time and continuous-time signals such as Fourier representations, Laplace and Z transforms, sampling; representations of linear, time-invariant systems such as difference and differential equations, block diagrams, system functions, poles and zeros, as well as impulse and step responses and frequency responses. Examples are drawn from engineering and physics, including the realms of feedback and control, communications, and signal processing. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites: ENGS142, ENGS151
Corequisites:
Program: BSES
Course Code: ENGS253
Title: Embedded systems Lab
Description: Hands-on laboratory course to reinforce concepts covered as well as provide system-level understanding. Students will conduct experiments in support of the topics covered in Embedded Systems.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS254
Title: Signals and Systems Lab
Description: Hands-on laboratory course to reinforce concepts covered as well as provide system-level understanding. Students will conduct experiments in support of the topics covered in Signals and Systems, with examples from engineering and physics, including the realms of feedback and control, communications, and signal processing.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS261
Title: Control Systems 1
Description: This course synthesizes fundamental electrical and mechanical principles in the analysis and design of control systems and control systems technology. Sensors, actuators, modeling of physical systems, design and implementation of feedback controllers; operational techniques used in describing, analyzing and designing linear continuous systems; Laplace transforms; response via transfer functions; stability; performance specifications; controller design via transfer functions; frequency response; simple nonlinearities. This course is intended to be taken concurrently with Control Systems 1 Lab. Instructor-led class time including problem sets as well as experimentation in a variety of controls applications.
Credits: 3.0
Prerequisites: ENGS252
Corequisites:
Program: BSES
Course Code: ENGS262
Title: Control Systems Lab
Description: Hands-on laboratory course to reinforce concepts covered as well as provide system-level understanding. Students will conduct experiments in support of the topics covered in Control Systems 1.
Credits: 1.0
Prerequisites:
Corequisites: ENGS261
Program: BSES
Course Code: ENGS264
Title: Control Systems 2 Lab
Description: Hands-on laboratory course to reinforce concepts covered as well as provide system-level understanding. Students will conduct experiments in support of the topics covered in Control Systems 2.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS265
Title: Mechatronic Design
Description: This course is to expose students to the fundamentals of mechatronics and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, and different types of actuators and their use. Instructor-led class time including problem sets, projects, and discussions.
Credits: 3.0
Prerequisites: ENGS241
Corequisites:
Program: BSES
Course Code: ENGS271
Title: Systems Engineering
Description: The Fundamentals of Systems Engineering is a transdisciplinary course that teaches about systems design principles and concepts using scientific, technological and management methods to enable successful realization, use and retirement of engineering systems.
It helps to better understand and document customer needs and required functionality early in the development cycle, then proceeding with design synthesis, conceptual design and development, system validation and verification while considering the complete problem including operations, performance, test, manufacturing, commissioning, cost, and schedule.
Topics include different hardware and software components of a system and how they interrelate and contribute to a system’s goals and success.
Assesment through problem sets, exams, and projects. Instuctor led discussions.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS275
Title: Resource Management
Description: nan
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS280
Title: Alternative Energy
Description: nan
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS281
Title: Flight and Orbital Mechanics
Description: Flight and Orbital Mechanics provides an integrated introduction to the physical principles and mathematical foundations governing the motion of aerospace vehicles in both atmospheric and space environments. The course covers the fundamentals of flight mechanics, examining the equations of motion, aerodynamic forces, propulsion, and performance parameters such as range, endurance, and flight envelope, before extending these concepts to orbital mechanics, where students analyze spacecraft motion under gravitational forces, orbital transfers, and perturbations. Through analytical derivations, simulations, and applied exercises, students gain a unified understanding of how forces and dynamics shape the trajectories, stability, and performance of aircraft and spacecraft.
Credits: 3.0
Prerequisites: ENGS142
Corequisites:
Program: BSES
Course Code: ENGS290
Title: Special Topics
Description: Course description tailored to topic when offered
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS298
Title: Capstone 1
Description: nan
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSES
Course Code: ENGS299
Title: Capstone 2
Description: This course provides Engineering Sciences majors the opportunity to develop the knowledge that they have obtained from across the curriculum. Students are encouraged to work in teams toward the implementation of an applied project, typically with industry partners on real life engineering problems under the mentorship of the advising instructor. Students will discuss each other’s projects at scheduled regular meetings led by the instructor. At the end of the course the projects will be presented and demonstrated orally and the project reports will be submitted in writing.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS101
Title: Introduction to Environmental and Sustainability Sciences
Description: The course introduces the basic principles of environmental and sustainability sciences, including the structure and functioning of ecosystems and their physical and biogeochemical cycles. The course will also examine these ecosystems within the context of complex socio-ecological and socio-technical systems. Specific topics include biodiversity, water, soil, land and air resources, human population dynamics, food and industrial production, and waste and toxicity. Topics will be supplemented by Armenia- and Caucasus-specific cases.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS102
Title: Modes of Inquiry in ESS
Description: The course introduces various methods of inquiry used in the field of Environmental and Sustainability Sciences. It is designed to equip students with tools and critical thinking skills to investigate, analyze, and explore paths to address complex challenges through the use of quantitative, qualitative, and hybrid approaches. Students will engage in hands-on exercises and case studies in ESS to enhance their practical skills. They will also develop abilities to critically evaluate existing research.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS103
Title: Research Methods and Statistics
Description: The course will develop the foundational skills for conducting research and analyzing data. Throughout the course, the students will explore quantitative methodologies and garner an understanding of statistical principles and techniques. Topics covered include normality, variance, correlation and regression analysis and data visualization. Hands-on exercises using statistical software will allow students to work on real-world datasets, fostering proficiency in data analysis. By the end of the course, students will identify relevant statistical tests based on population and sample characteristics.
Credits: 3.0
Prerequisites: ESS102
Corequisites:
Program: BSESS
Course Code: ESS110
Title: Environmental and Natural Resource Economics
Description: The course introduces core economic concepts, including supply and demand, market equilibrium, public goods, and externalities. It also explores the challenges faced by modern societies to ensure economic development while preserving and regenerating the natural environment. Students will learn about key analytical tools (such as cost-benefit analysis) utilized by economists when designing and evaluating environmental policies and will be asked to apply them critically to real-world issues and policies. Topics include linkages between the market economy and the environment, challenges of energy transition, and methods for the valuation of environmental costs and benefits.
Credits: 3.0
Prerequisites: ESS101
Corequisites:
Program: BSESS
Course Code: ESS120
Title: Biology and Ecosystems
Description: This course provides students with a solid foundation in the fundamental biological principles that govern environmental sustainability, from the smallest building blocks (genes & cells) to the interactions between organisms, populations, communities, and their abiotic surroundings. It will examine the role of biodiversity in human life and healthy ecosystems, discussing its conservation and restoration globally, in the Caucasus region, and in Armenia.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS120L
Title: Biology and Ecosystems Laboratory
Description: The course trains students in biology laboratory equipment use and techniques as well as field research approaches to bolster the underlying concepts discussed in the lecture course.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS125
Title: Chemistry for Environment and Sustainability
Description: The course introduces basic chemistry concepts, including bonding, molecular structure, chemical reactions, thermochemistry, and chemical kinetics. In addition, students will gain skills in data mining and analysis related to environmental chemistry. All these will be applied to understand chemical systems including biogeochemical cycles, pollution, food and consumer goods, and toxicity.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS125L
Title: Chemistry for Environment and Sustainability Lab
Description: The course trains students in chemistry laboratory equipment use and techniques to bolster the underlying concepts discussed in the lecture course. In addition, the lab will hone student skills in observing, critically assessing, and reporting data.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS130
Title: Environmental Geology
Description: The course provides students with a basic understanding of geological processes that control our environment. Topics to be included are Earth’s structure and plate tectonics, rocks and minerals, weathering and erosion, sediments and soils, geohazards, and engineering geology. The course will also address geological resources (metals, minerals, and fossil fuels) and their extraction from a sustainability perspective.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS130L
Title: Environmental Geology Lab
Description: This course provides students with hands-on experiential learning in support of the topics covered in ESS130. Laboratory exercises will involve the tools typically used in geology and environmental science.
Credits: 1.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS140
Title: Sustainable Energy Systems and Solutions
Description: The course delves into the sustainable generation and use of energy at various scales, including building, local, national, and transnational levels. Key topics encompass energy efficiency, centralized and distributed energy generation, smart grids, non-fossil fuel transportation, energy storage, energy markets, and sustainable energy policies. Students will examine these topics from environmental, economic, and social perspectives. The course is project-based, allowing students to apply their knowledge through individual or group projects. Assessment will include these projects, as well as quizzes and examinations, ensuring a grasp of key topics.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS160
Title: Sustainable Food Systems
Description: The course focuses on human food systems, including their social, economic, and environmental sustainability aspects. Students will become familiar with primary agricultural resources and inputs, production technologies, post-harvest handling, and food waste, logistics, and marketing. They will also become familiar with developments in the food industry such as genetically modified organisms, organic agriculture (including sustainable fertilizer and pesticide management), fair trade, plant-based diets, and approaches to reduce food loss. The course is project-based, allowing students to apply their knowledge through individual or group projects. Assessment will include these projects, as well as quizzes and examinations, ensuring a grasp of key topics.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS180
Title: Introduction to Geographic Information Systems(GIS) and Remote Sensing
Description: The course introduces geographic information systems (GIS) and remote sensing using satellite images. Students gain skills in spatial analysis, including collecting and problem-solving through the use of visualization and analytical tools. More and more industries rely on GIS and remote sensing to analyze and visualize data. This course will look at applications of GIS in environmental sciences, public health, sustainable transportation, land-use planning, telecommunications, hydrology, meteorology, crime patterns, etc. The course will also explore remote sensing (Earth Observation) tools offered by NASA, EU Copernicus, and private-sector satellite imagery.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS200
Title: Environmental Monitoring
Description: The course introduces students to the principles and practices of environmental monitoring and its role in assessing ecosystem health, supporting policy compliance, and managing natural resources. Students will learn best practices across environmental domains: monitoring air, water, and soil contamination, with a strong focus on quality control, including instrument calibration and documentation. The course also examines challenges in data management and interpretation. Learning activities include discussions, readings, data mining, report writing, presentations, and practical assignments.
Credits: 3.0
Prerequisites: ESS101, ESS102, ESS120 OR ESS125 OR ESS130
Corequisites:
Program: BSESS
Course Code: ESS208
Title: Environmental and Sustainability Modeling
Description: The course focuses on theoretical background as well as skills to develop and apply models in the context of complex environmental and social systems. The course will explore the strengths and limitations of various modeling approaches. It will equip students with the ability to discern their appropriateness for specific contexts. The course is intended for students with limited mathematical background.
Credits: 3.0
Prerequisites: ESS103, ESS110, ESS120 OR ESS130
Corequisites:
Program: BSESS
Course Code: ESS215
Title: Climate Change Science and Politics
Description: The course covers climate change from a multidisciplinary perspective to understand its causes and consequences as well as needed responses. The course will explore the science and politics of climate change. Key international and Armenia and Caucasus-specific literature, case studies, and social and political movements around climate change will be reviewed and discussed. The course will require students to participate in a simulated multi-stakeholder and multinational negotiations on addressing climate change.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS240
Title: Sustainable Cities
Description: The course introduces the concept of sustainable cities—places with human prosperity, social equity, and environmental health. Special emphasis will be placed on the concepts and tools necessary to address the environmental sustainability of cities, including their resource metabolism, ecology, and built environment impacts. Using case studies of cities, towns, and development projects globally, students will have the opportunity to reflect on principles of sustainability and innovative applications used at various scales by planners and designers. Students are expected to collect, analyze, and present data as well as assess the merits of analyses by others.
Credits: 3.0
Prerequisites: ESS101
Corequisites:
Program: BSESS
Course Code: ESS244
Title: Water
Description: The course examines water from various perspectives including ecological, human health, resource stewardship, economic, legal, and political. Topics to be covered include water supply, use, and recycling in agriculture, manufacturing, mining, energy, and domestic life; potential for resource efficiency and optimization; water quality and types of water pollution, methods of testing and monitoring water quality and conditions of freshwater ecosystems; water purification and wastewater treatment; water planning and management tool including those for watersheds, surface, and groundwater resources; new technologies, such as desalination, to access freshwater; and international and national laws on water. The course is project-based, allowing students to apply their knowledge through individual or group projects. Assessment will include these projects, as well as quizzes and examinations, ensuring a grasp of key topics.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS246
Title: Solid Waste in Circular Economy
Description: The course will enable students to understand and explore principles of sustainable waste management from environmental, technological, social, and economic viewpoints. The course covers various types of waste, including food, packaging, plastics, paper, clothes, electronics, automotive, agricultural, and more. The course will focus on the circular economy solutions discussing waste reduction strategies, green product design, reuse and recycling practices, zero-waste lifestyle, waste-to-energy, composting, biogas production, and more. The course is project-based, allowing students to apply their knowledge through individual or group projects. Assessment will include these projects, as well as quizzes and examinations, ensuring a grasp of key topics.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS250
Title: Biodiversity Conservation and Restoration
Description: The course explores biodiversity conservation and restoration, combining foundational knowledge with hands-on experience. Students will dive into essential conservation policy tools, cutting-edge field research techniques, and innovative restoration approaches. They will analyze real-world conservation challenges and discover effective strategies to conserve and restore biodiversity. The course includes lecture hours, field visits, and group projects with conservation organizations or professionals.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS262
Title: Environmental and Sustainability Governance
Description: The course introduces students to environmental and sustainability governance systems at the international, national, and local levels. Students critically analyze legal and institutional mechanisms, including international conventions and their implementation, Armenian constitutional and legislative frameworks, and local regulations. The course includes two practice-oriented mock cases based on real-world scenarios to develop applied skills in environmental governance.
Credits: 3.0
Prerequisites: ESS101
Corequisites:
Program: BSESS
Course Code: ESS270
Title: Disasters and Resilience Management
Description: The Course explores the history, principles, theories, and approaches of resilience frameworks and management. Students study natural hazards–earthquakes, floods, wildfires, landslides, etc.–and anthropogenic hazards–industrial, nuclear, cyberterrorism, massive explosions, etc.– to plan and implement prevention, preparedness, response, recovery, and mitigation strategies and tools. Topical investigations include a range of physical and human-related impacts of disasters, the role of decision-makers and the general public, and social and technological aspects of improved resilience. Global and Armenia-specific cases and scenarios are discussed.
Credits: 3.0
Prerequisites:
Corequisites:
Program: BSESS
Course Code: ESS283
Title: Environmental and Sustainability Assessment Tools
Description: The course examines tools used to assess and mitigate the environmental and social impacts of products, operations, projects, and policies. It will review examples of commonly used tools, e.g., Environmental Impact Assessment (EIA), Strategic Environmental Assessment (SEA), and Life-Cycle Assessment (LCA). Emphasis will be on learning from real cases in Armenia, the Caucasus region, and globally.
Credits: 3.0
Prerequisites: ESS120L, ESS125L, ESS130L
Corequisites:
Program: BSES
Course Code: IESM106
Title: Probability and Statistics
Description: The topics covered in this introductory course include: axioms of probability; conditional probability, independence; combinatorial analysis; random variables and distributions; expectation, variance, covariance; transformation of random variables; limit theorems, the law of large numbers, the central limit theorem; Markov chains; applications; statistical estimation; correlation, regression; hypothesis testing, maximum likelihood estimation, Bayesian updating; applications. Students are required to complete problem sets in order to demonstrate rudimentary foundational knowledge in mathematical modeling and to apply practical analytical and numerical methods to solve problems in computational sciences. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM220
Title: Operations Research 1
Description: Decision making with constrained resources, including product mix, scheduling, and manufacturing models, project planning, and planning with uncertain futures. The course also introduces analysis of network-based models such as vehicle routing, as well decision problems with opposition (game theory). This course concentrates on the classical linear programming (LP) model as a solution method, and introduces extensions of LP that accommodate logical decisions, in particular mixed-integer programming (MIP). Familiarity with basic linear algebra and a programming language is required.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM301
Title: Analysis and Design of Data Systems
Description: Three hours of lecture per week. Review of data systems and data processing functions; technology; organization and management; emphasizing industrial and commercial application requirements and economic performance criteria; survey of systems analysis, design; modeling and implementation; tools and techniques; design-oriented term project.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM311
Title: Quality Assurance and Management
Description: Three hours of lecture per week. Principles and methods of statistical process control, quality engineering, total quality management, as applied to manufacturing and service industries.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM313
Title: Data Mining & Predictive Analytics
Description: Exploratory Data Analysis; Classification: Decision Trees, Model Evaluation, Overfitting; Linear and Logistic Regression; Association Analysis; Cluster Analysis; Anomaly Detection; Model Building and Validation
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM314
Title: Business Intelligence Systems and Analytics
Description: This course introduces the concepts and practices of Business Intelligence (BI) with a strong emphasis on using Structured Query Language (SQL) for data preparation and tools for data visualization and analytics. Students will learn the BI lifecycle from data sources to dashboards, explore data modeling and governance, and progressively master BI tools — starting from fundamentals to advanced analytics, parameters, Level of Detail (LOD) expressions, and storytelling dashboards. The course is hands-on, with frequent practical sessions and a final project applying BI to a real-world industrial dataset. Prior knowledge of database systems and proficiency in SQL are required.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM315
Title: Design and Analysis of Experiments
Description: Three hours of lecture per week. Principles and methods of design and analysis of experiments in engineering and other fields, realworld applications of experimental design, completely randomized designs, randomized blocks, latin squares, analysis of variance (ANOVA), factorial and fractional factorial designs, regression modeling and nonparametric methods in analysis of variance.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM321
Title: Operations Research 2
Description: Deterministic and stochastic models and methods in Operations Research, network analysis, integer programming, unconstrained and constrained optimization, deterministic and stochastic dynamic programming, Markov chains, queuing theory.
Credits: 3.0
Prerequisites: IESM220
Corequisites:
Program: MEIESM
Course Code: IESM324
Title: Applied Statistics for Engineers
Description: This course starts by introducing the probability laws as a foundation for statistical inference in engineering. The concept of the likelihood function in an engineering model is illustrated. The course provides a substantial coverage of propagation of error, as well as an emphasis on model-fitting. The use of simulation methods and the bootstrap is made for verifying normality assumptions, estimating bias, computing confidence intervals, and testing hypotheses. In the second part of the course, diagnostic procedures are introduced for linear regression models including material on examination of residual plots, transformations of variables, and principles of variable selection in multivariate models. The analysis of data from a class of experiments is discussed along with statistical quality control. Instructor led lectures and discussion. Assessment by problem sets, exams, and projects.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM325
Title: Decision Analysis
Description: Three hours of lecture per week. Formulation, analysis and use of decision-making techniques in engineering; operations research and systems analysis; decision trees and influence diagrams; Bayesian decision theory; utility theory; multiple-attribute decision analysis; introduction to Game Theory.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM330
Title: Simulation of Industrial Engineering Systems
Description: Three hours of lecture per week. Design, programming and statistical analysis issues in simulation study of industrial and operational systems, generation of random variables with specified distributions, variance reduction techniques, statistical analysis of output data, case studies, term project.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM331
Title: Production Systems analysis
Description: Three hours of lecture per week. Analysis, design and management of production systems. Topics covered include productivity measurement; forecasting techniques; project planning; line balancing; inventory systems; aggregate planning; master scheduling; operations scheduling; facilities location; and modern approaches to production management such as Just-In-time production.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM340
Title: Engineering Economics
Description: Three hours of lecture per week. Analysis of economic investment alternatives, concepts of the time value of money and minimum attractive rate of return, cash flow analysis using various accepted criteria, e.g., present worth, future worth, internal rate of return, external rate of return, depreciation and taxes, decision making under uncertainty, benefitcost analysis, effects of inflation (relative price changes).
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM345
Title: Supply Chain Management
Description: This course focuses upon the strategic importance of supply chain management. The purpose of the course is to design and manage business-to-business to retail supply chain purchasing and distribution systems, and to formulate an integrated supply chain strategy that is supportive of various corporate strategies. New purchasing and distribution opportunities for businesses and inter/intra company communications systems designed for creating a more efficient marketplace are explored.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM346
Title: Managing Engineering and Technology
Description: Managing Engineering and Technology is designed for engineers, scientists, and other technologists interested in enhancing their management skills, and for managers in enhancing their skills and knowledge about engineering and science. Specifically, the course is tailored to the needs of technical professionals and will cover: the historical development of management with an emphasis on the management of technology, management methods and tools, transition from technical performer to technical management, and the nature and application of management principles throughout the technology product/project life cycles. The course will be based on a mix of theory, empirical evidence and real-life cases. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM347
Title: Design and Innovation of Information Services
Description: The course aims to provide with theoretical and practical insight into the key concepts and issues that guide the design and development of modern information services. The students will explore the contextual considerations of designing information services through in-depth examination of expanding possibilities for innovation and associated risks that modern-day devices, data, content, systems and infrastructures offer. Of particular interest will be the structuring and design of problems in industries with complex ecosystems using Soft Systems Methodology and Unified Modeling Language with special stress on capturing and analyzing information requirements of parties involved. No prerequisite knowledge is required. As part of the course, students will design their own information service to address a problem of their choice, using all the depth of technical and social issues facing companies, individual users and societies.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM348
Title: Technology, Ethics and Society in the Age of AI
Description: This survey course will address an issue that humans have faced since the earliest form of technological innovation, starting with the discovery of fire and crude weapons: as new technologies are developed, can we as individuals and societies adapt to manage the impact of these technologies on our lives? While many inventions, from the printing press to the combustion engine to electricity to the atomic bomb have been available to a select few elites, the power of artificial intelligence is now at the fingertips of almost anyone with a connected device, and has sky-rocketed in its impact on everything from education to medicine to creating content to waging war. How can we make sure that we, as future creators and consumers of AI-enabled products and services, have the right tools and context to make sure that what we create, or utilize, does more good than harm? While the topic is vast, we will jointly explore concrete examples from different sectors to help us become more informed participants in this latest technology arms race. The course will consist of a combination of lectures, case study discussions, screening and discussion of movies and TV shows, as well as guest speakers.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM349
Title: Enabling Competitive Advantage through Information Technology
Description: This class is intended to introduce students to the critical role of information technologies (IT) in enabling competitive strategies. Our particular focus will be the impact that IT can have on non-IT companies, from industries such as transportation, supermarkets, financial institutions, and healthcare. This is not a “how-to” guide on managing enterprise information systems. Rather, the focus is on the word Enable, and we will explore how different companies have used IT to develop significant competitive advantage in the marketplace. The course will consist of case readings and discussions, short assignments, group project, and mid-term and final exams.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM350
Title: Alternative Energy
Description: The course reviews: the basics of the alternative energy generation options, the respective technologies and resources, as well as the economic, environmental and urban aspects of their introduction into the modern society. Topics include: the role and the current status of the alternative energy in the modern society, energy and force – phenomena and units, solar radiation characteristics, carbon cycle and traditional sources of energy, solar thermal processes (options), such as wind, solar heat, ocean heat and wave, solar hot water, solar electricity, passive solar, solar photon processes, such as solar photovoltaics – from principles to systems, biomass, biofuel, biogas, etc, nuclear power – fusion and fission, infrastructure related economics, distributed power, energy storage, etc.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM351
Title: Sustainable Smart and Resource Efficient Systems 1: Systems and Technologies
Description: The course introduces students to the latest practices and technologies in reducing the environmental impact of buildings and the built environment with specific focus on energy, water, and waste. Students will be expected to gain analytical and quantitative skills in analyzing energy, transport, water, and solid waste with the aim of estimating ways to achieve “carbon neutrality,” “zero emissions,” among other green goals. Students will also be introduced to green built environment norms established by the US Green Building Council as well as other international companies.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM352
Title: Sustainable Smart and Resource Efficient Systems 2: Decision Making Tools
Description: The course will focus on non‐design decision tools. The analytical tools to be covered will include financial (payback period, NPV, and IRR), economic (Input‐Output, Cost‐Benefit), and environmental (Life Cycle Assessment, McKinsey Carbon Abatement Analysis, Carbon Footprint, Water Footprint, Ecological Footprint). Many of these analyses will be relevant for a wide range of industries including transportation, construction, manufacturing, as well as energy. The course will use cases and simulations to teach and deepen understanding of core concepts and methodologies.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM360
Title: Computer-Aided Design
Description: Fundamentals of part design; computer-aided design tools and data structures; geometric modeling; transformations; CAD/CAM data exchange; mechanical assembly.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM361
Title: Computer Aided Manufacturing
Description: Introduction to manufacturing processes; cutting fundamentals; design for manufacturability; design for machining; process engineering; NC fundamentals; manual NC programming; computer-aided part programming; group technology.
Credits: 3.0
Prerequisites: IESM360
Corequisites:
Program: MEIESM
Course Code: IESM362
Title: Advanced CAD/CAM Applications
Description: Advanced surface and solid modeling, top down and bottom up assembly, finite element analysis, sensitivity studies, optimization, advanced computeraided part programming and manufacturing, mold design, team work.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM372
Title: Portfolio Theory and Risk Management
Description: Students in this course will become familiar with the basic concepts of interest theory, portfolio theory and risk assessment and be able to apply these in problem solving, with an emphasis on mathematical and computational approaches. The student will also become acquainted with various financial risk management instruments and use different criteria to optimize portfolios, taking into consideration the strengths and weaknesses of different portfolio selection criteria. Instructor led lecture and discussions; assessment may include problem sets, software implementation, exams, and projects.
Credits: 3.0
Prerequisites: IESM220 OR CS213 or equivalent
Corequisites:
Program: MEIESM
Course Code: IESM390
Title: Integrative Project in Modern Production Methods
Description: Two hours of lecture and discussion and six hours of field work per week. This is a projectbased course that involves field work (in manufacturing or service organizations) and integrates and synthesizes knowledge gained from several courses (e.g., operations management, operations research, statistics, and quality management). Student teams, supported by several faculty members, will work with industrial companies to identify improvement opportunities and help in implementing them.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM391
Title: Independent Study
Description: Special study of a particular problem under the direction of a faculty member. The student must present a written, detailed report of the work accomplished. Approval of the IESM Program Chair and the instructor is required.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM392
Title: Special Topics in IESM
Description: This course explores emerging and advanced topics in Industrial Engineering and Systems Management that extend beyond the regular curriculum. The focus varies by semester and may include areas such as data analytics, decision modeling, advanced statistical modeling, or technology-driven process improvement. Through case studies, hands-on projects, and research discussions, students gain exposure to contemporary challenges and innovative methodologies shaping the field.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM395
Title: Capstone Preparation
Description: Review of Capstone objectives and procedure; faculty and industry representatives’ presentation of suggested research topics; field trips to the local companies; literature survey and classroom presentation by students. Students select the topic of their capstone project and the supervisor and prepare and submit the project proposal. Students draft a literature survey on their selected topic, which will constitute a section or chapter of the capstone project report. The completed and approved Proposal for Culminating Experience Requirement form must be filed in the College office prior to the end of the course.
Credits: 2.0
Prerequisites:
Corequisites:
Program: MEIESM
Course Code: IESM396
Title: Capstone: Thesis
Description: One of the two Capstone options offered by the Program. Supervised individual study employing concepts and methods learned in the program to solve a problem of significant importance from a practical or theoretical standpoint. This option is more appropriate for those students who are interested in an in-depth R&D experience.
Credits: 4.0
Prerequisites: IESM395
Corequisites:
Program: MEIESM
Course Code: IESM397
Title: Capstone: Project
Description: One of the two Capstone options offered by the Program. Supervised individual study employing concepts and methods learned in the program to solve a problem from a practical standpoint. This option is more appropriate for those students who are inclined to practical work and do not necessarily aspire for intensive research training.
Credits: 1.0
Prerequisites: IESM395
Corequisites:
Program: MSCIS
Course Code: CS302
Title: Functional Analysis
Description: The course gives an introduction to functional analysis, which is a branch of mathematics in which one develops analysis in infinite dimensional vector spaces. The main areas to be covered are normed spaces with an emphasis on Banach and Hilbert spaces. Students will be introduced to fundamental theorems related to Banach spaces: The Hahn-Banach, Fixed point, Uniform Boundedness Principle, Open Mapping and Closed Graph theorems. This course will provide also an overview of Spectral theory for compact operators with applications in integral and differential equations. Instructor-led class time including discussions and problem sets; assessment by exams and problem sets.
Credits: 3.0
Prerequisites: CS103 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS310
Title: Theory of Computing
Description: Theory of computation comprises the fundamental mathematical properties of computer hardware, software, and applications. This theory deals with computational models (or abstract machines) and investigates computational power of these models. The finite automata, pushdown automata and Turing machines are the computational models that are widely used in applications and theoretical research. This course aims to provide students with a foundation for using these models both for practical and theoretical needs.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS311
Title: Theory of Algorithms
Description: Review of main abstract data types. Sorting algorithms: correctness, space and time complexity. Graph algorithms. Algorithmic Paradigms: divide-and-conquer, greedy, dynamic programming. NP-completeness and approximation algorithms. The course aims at providing students with the tools and techniques for designing efficient algorithms.
Credits: 3.0
Prerequisites: CS121 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS312
Title: Object-Oriented Analysis and Design
Description: The UP (Unified Process) and the principle of iterative and incremental software development, UP artifacts, usage of UML (Unified Modeling Language) notation for representation results of analysis and design, studying and applying of design patterns, usage of CASE (ComputerAssisted Software Engineering) tools to aid in analysis and design.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS313
Title: Advanced Topics in Algorithms
Description: This course will review basic paradigms of algorithm design such as divide-and-conquer, dynamic programming, greedy algorithms, graph algorithms; and then explore some of the more advance topics such as Network Flow and Bipartite Matchings, NP-completeness, Approximation Algorithms, and other selected topics. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS314
Title: Theory of Communication Networks
Description: The course investigates several communication problems in networks; one-to- all, all-to- all, one-to- many. Specific communication models are considered by placing constraints on the sets of messages, senders, and receivers, on the network’s topology, on the rules that govern message transmissions, and on the amount of information about the network known to individual network members. One goal is to design network structures which are inexpensive to construct yet allow fast communication. The second major goal is to design efficient communication algorithms for commonly used networks under different communication models. These require knowledge of graph theory, combinatorics, and design and analysis of algorithms. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS121 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS315
Title: Cryptography
Description: Introduction of basic principles and methods of modern applied cryptography. Demonstration how cryptography can help to solve information security problems and our focus will be basically internet security. Students will learn to understand and evaluate real life security problems that cryptography can solve. They will also discuss various open problems in applied cryptography. Finally, students will implement cryptographic primitives used in common real applications. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS316
Title: Advanced Cryptography
Description: This course will introduce alternative, more efficient, and non- traditional public-key cryptosystems. Students will get acquainted with white box cryptography essentials. Other topics to be covered: a) cryptographic primitives related to cloud computing, in particular a secure search over encrypted data; b) homomorphic encryption methods; c) identity based encryption; and d) secure multi-party computation protocols. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS315
Corequisites:
Program: MSCIS
Course Code: CS317
Title: Computer Graphics
Description: The course provides students with theoretical and applied tools in graphics development. The course examines topics including: geometric concepts, such as tangent plane, normal vector; pixel-related operations; interactive methods, such as mouse and keyboard callback functions; representation of graphics primitives; general introduction to Open GL as a State Machine; various shading algorithms to illustrate the rendering process; color calculations; texturing. Coursework will include such assignments as critical review of current trends in the field, implementations of theories, or group projects. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS318
Title: Advanced Topics in the Theory of Computation
Description: Course Description tailored to course content when offered.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS319
Title: Computer Vision
Description: This course offers an introduction to Computer Vision, an emerging interdisciplinary field that includes methods for acquiring, processing, analyzing of digital images and videos and extracting useful information from them. Students will learn basic methods that include exploring known models in image representations, depth recovery from stereo, camera calibration, image stabilization, automated alignment, tracking, edge detection, and pattern recognition. They will also develop statistical models for image classification, clustering, and dimensionality reduction. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS108 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS322
Title: Software Engineering
Description: Software life cycle processes including analysis, design, modifying and documenting large software systems. Topics include software development paradigms, system engineering, function-based analysis and design, and object-oriented analysis and design. Students will implement a working software system in a team environment.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS323
Title: Advanced Object-Oriented Programming
Description: Basic principles of object oriented analysis and design utilizing UML, advanced object oriented programming principles, design patterns, frameworks and toolkits; Agile software design processes. Development of a mid-size programming project working in teams..
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS325
Title: Development of Geo-Collaborative Applications
Description: The students acquire basic knowledge for developing web-based geo-collaborative application for supporting decision making processes. Students learn the basic concepts of cartography and the most common client and server side programming resources which are used for web-based geo-collaborative application development. Students have to solve small tasks during classes as well as develop a mid-size programming project working in teams. They learn to integrate the most common free maps resources (Google Maps and Open Layers) and geographic data sources (Open Street Maps) in their application as well as free available geographic database (PostGis). Instructor-led discussions and problem sets.
Credits: 2.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS326
Title: Database Systems
Description: Introduction to databases, the Entity-Relationship (ER) Model and conceptual database design, the relational model and relational algebra (RA), SQL. Topics include data storage, indexing, and hashing; cost evaluating RA operators, query evaluation as well as transaction management, concurrency control and recovery; relational schema refinement, functional dependencies, and normalization; physical database design, database tuning; security and authorization of parallel and distributed database systems; data warehousing and decision support, views. In addition, introduction to Data Mining and various applications will be covered. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS327
Title: Parallel and High-Performance Computing (Parallel HPC)
Description: The course examines topics including: parallel hardware architectures, distributed computing paradigms, parallelization strategies and basic parallel algorithmic techniques, parallel programming with OpenMP and MPI, HPC numerical libraries. Students should be able to demonstrate advanced knowledge related to contemporary methods in parallel and HP Computing. Students are required to draw upon investigative techniques related to this field in order to critically analyze and solve problems using advanced knowledge. Coursework will require students to develop faster codes that are highly optimized for modern multi-core processors and clusters. Three hours of instructor-led class time per week including discussions, lab work and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS328
Title: Human Computer Interaction
Description: The topics include: concepts of human computer interaction, techniques for user interface design; user-centered design, interface development techniques, usability evaluation; overview of interface devices and metaphors; visual development environments, other development tools. Students should be able to demonstrate advanced knowledge of software and hardware systems related to computational sciences. Students should also be able to formulate and critically assess problems and sub-tasks including identification of sources and investigative techniques related to the field. Students are required to complete group projects in which they formulate, critically assess, and investigate problems relating to software and hardware systems. Masters students will complete formal presentations commensurate with their knowledge level in order to develop experience communicating to audiences both within and outside the discipline. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS329
Title: Data Warehousing
Description: An advanced hands-on course in Data Warehousing which will build upon knowledge gained in an introductory course in Databases. Topics covered include Data Warehouse architectures, multidimensional data representation and manipulation, Data Warehouse design practices and methodologies, creation of Extract-Transformation-Load (ETL) workflows, with emphasis on data governance practices, business intelligence concepts and platform capabilities, and visualization tools. Instructor-led hands-on laboratory class time with assessment based on discussions, problem sets, projects, and significant in-laboratory applications.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS331
Title: Operating Systems
Description: The organization and structure of modern operating systems. System level programming in Windows and Unix Operating Systems.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS332
Title: System Administration
Description: User administration. Operating system installation, tuning and control. Network administration. Security management. Performance tuning and management.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS333
Title: Network Programming
Description: Students will acquire skills for developing distributed applications running over TCP/IP networks. Students learn the basic concepts of networking client-server programming as well as advanced topics such as concurrent serving, state vs. non-state servers, multicasting, peer-to- peer architectures. Instructor led in-class projects, and development of a mid-size programming team project.
Credits: 3.0
Prerequisites: CS121
Corequisites:
Program: MSCIS
Course Code: CS334
Title: Performance Analysis and Queueing Theory
Description: The course reviews basics of probability theory, stochastic processes, especially Markov chains, and Laplace and z-transforms before proceeding with the analysis of queueing systems. After introducing basic laws of queueing theory, such as Little’s result, the analysis of single- and multi-server quueing systems is dicsussed. Also product-form open and closed queueing network models and efficient methods for their analysis: the convolution algorithm and mean-value analysis. Principles of descrete simulation methods are discussed to deal with systems not lending themselves to queueing analysis. The emphasis of the course is gaining insight into the behavior of systems with various workloads.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS335
Title: Introduction to EDA
Description: Structure of modern VLSI chips. Basic understanding of VLSI device manufacturing process. Overview VLSI chip design flow, including the System-Level design and interaction with SW and FW development process and teams. Understanding of modern SoC architectures: FW, SW, HW levels. Specifics for Analog-mixed-signal, CPU/RAM and other HW fabrics, and ASIC. Overview of digital circuits, standard cells. Digital design, standard-cell design. Overview of the Front-end and back-end. Detailed review of the back-end design phases. Introduction to EDA tools SW architecture: data layer, user-interface, algorithmic layer. Introduction to basic design patterns and architectures for DB and UI design for EDA tools. Overview of algorithms and data structures used in EDA. Detailed overview of back-end problems, and their corresponding mathematical problem formulations from combinatorial optimization, computational geometry, mathematical programming. Detailed study on concrete examples. Overview of simulation and analysis techniques. Detailed study of concrete examples.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS336
Title: Compiler Design
Description: An introduction to the basic phases of modern compilers and their design principles. Topics covered include CPU instruction, finite state machines, lexical scanning, parsing schemes, code generation and translation, comparison of modern programming languages, and an analysis of the relationship between compilers and operating systems. As part of the course, students build a working compiler for an object-oriented language. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS130 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS337
Title: Cybersecurity
Description: This course covers various security risks in Cyberspace from both offensive and defensive points of view, including subfields such as Web/Mobile Security, Network Security, and Cryptography. Students will develop skills of usage of various tools to be able to test the security of systems as well as build defense for those. Students will lead a team project in one of the following subfields (eg Web Security, Mobile Security, IoT security, Digital Forensics). Instructor-led discussion, along with reading, written, and practical assignments. Assessment via exams projects and hometasks.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS338
Title: Distributed Systems
Description: Distributed systems help programmers aggregate the resources of many networked computers to construct highly available and scalable services. The course covers general introductory concepts in the design and implementation of distributed systems, covering all the major branches such as Cluster Computing, Grid Computing and Cloud Computing. The main principles underlying distributed systems will be investigated: processes, communication, naming, synchronization, consistency, fault tolerance, and security. The course gives some hands-on experience as well as some theoretical background. Moreover the course will go in deep of several technical issues in cloud systems, such as Amazon EC2/S3, and Hadoop (MapReduce framework). Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites: CS311
Corequisites:
Program: MSCIS
Course Code: CS339
Title: Quantum Computing
Description: The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. After developing the basics, this course delves into various implementation aspects of quantum computing and quantum information processing including the quantum fourier transform, period finding, Shor’s quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. Instructor-led discussion, along with reading, written, and practical assignments. Assessment via problem sets, projects and exams.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS340
Title: Machine Learning
Description: This course introduces the fundamental concepts and methods of machine learning, focusing on how computers can learn from data to uncover hidden insights without explicit programming. Topics include supervised learning, unsupervised learning, and best practices in machine learning, with numerous examples from real-world applications. The course includes instructor-led discussions and problem sets to reinforce the material.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS341
Title: Blockchain Technologies
Description: This course introduces the mathematical and algorithmic foundations behind blockchain technology, culminating in hands-on work with a modern blockchain development stack. Students first study core cryptographic primitives including one-way hash functions, symmetric-key ciphers, public-key encryption, and digital signature schemes emphasizing formal security notions and common implementation pitfalls. It then covers the core components of blockchain technology such as block creation, mining, transaction processing, and Merkle trees, as well as consensus mechanisms like Proof-of-Work and Proof-of-Stake. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS211
Corequisites:
Program: MSCIS
Course Code: CS342
Title: Data Science
Description: This course aims to introduce students to the world of data science. Students will gain the skills that are transforming entire industries from healthcare to internet marketing and beyond. In this course, students will gain a hands-on introduction to using R programming language for reproducible data analysis. Students will define the data science process, including data acquisition, data munging, exploratory data analysis, visualization and modeling real world data. The course will include using R and R packages tools for analysis of both structured and unstructured data sources, as well as writing reproducible data analysis reports with R Markdown and creating personalized interactive graphics applications. Coursework will include such assignments as critical review of current trends in the field, implementations of theories, or group projects. Instructor-led discussion, along with reading, written, and practical assignments.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS343
Title: Data Visualization
Description: Visualization is increasingly important in this era where the use of big data is growing in many different fields. This course is designed to introduce methodologies and tools for transforming the data into interesting and insightful visual representations, including interactive web visualizations. Students will learn basic visualization design and evaluation tools and techniques, and learn how to acquire, parse, and analyze large datasets. Students will also learn techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/graph-based data. Additionally, students will utilize tools such as R and ggplot2 to prototype many of these techniques on existing datasets. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS344
Title: Applications in High-Performance Programming
Description: nan
Credits: 3.0
Prerequisites: CS121 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS345
Title: Bioinformatics
Description: The course starts with a brief introduction to molecular biology. The course then investigates the main algorithms used in Bioinformatics. After a brief description of commonly used tools, algorithms, and databases in Bioinformatics, the course describes specific tasks that can be completed using combinations of the tools and Databases. The course then focuses on the algorithms behind the most successful tools, such as the local and global sequence alignment packages: BLAST, SmithWaterman, and the underlying methods used in fragment assembly packages.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS346
Title: Artificial Intelligence and Decision Support
Description: This course provides an introduction to decision support techniques in the context of artificial intelligence. The main areas to be covered are knowledge-based agents, planning, reasoning under uncertainty and decision theory. Students will learn the principles of intelligent agent-based systems and implement agent programs that show rational behavior. Students will also learn logic programming. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS347
Title: Knowledge Representation
Description: Knowledge representation (KR) is the study of how knowledge about the world can be represented in a computer system and what kinds of reasoning can be done with that knowledge. Challenges of KR and reasoning are representation of commonsense knowledge, the ability of a knowledge-based system to tradeoff computational efficiency for accuracy of inferences, and its ability to represent and manipulate uncertain knowledge and information. This course will provide an overview of existing representational frameworks developed within AI, their key concepts and inference methods. It will also discuss some non-classical logical frameworks, such as non-monotonic logics. One of the objectives of the course is to help students understand how the theoretical material covered in the course is currently being applied in practice. Instructor-led class time including problem sets and discussions.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS350
Title: Software Project Management
Description: Methods and procedures for managing a software development project. Includes notions of project planning, time, cost and resource estimation, project organizational types, staffing (team assembly) and training considerations, leading and motivating computer personnel, and methods for monitoring and controlling the progress of a project. Quality management and risk assessment are considered. Case Studies of successes and failures will be studied.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS352
Title: Technical Leadership
Description: This course equips students with the leadership and management skills required to guide technical teams in dynamic environments. Emphasizing soft skills, strategic thinking, and high-level technical alignment, topics include effective communication, team-building, agile methodologies, product engineering, and ethical decision-making. Students will learn to balance technical and people leadership through reflective practices and collaborative projects.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS355
Title: Entrepreneurship
Description: Seminar exploring the complexities of creating and sustaining an entrepreneurial venture. We concentrate on the impact of innovative behavior and its implication to decision making. The primary focus of the course is on the behaviors involved in forming new enterprises: recognizing and evaluating opportunities, developing a network of support, building an organization, acquiring resources, identifying customers, estimating demand, selling, writing and presenting a business plan, and exploring the ethical issues entrepreneurs face. The course consists of case studies and discussion, inclass exercises, readings, guest speakers, and an outside project.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS360
Title: Computational Methods
Description: The course will cover topics including: matrix norms and iterative methods for linear systems and eigenvalue problems, numerical solutions of nonlinear equations and systems, numerical optimization methods, interpolation and approximation of functions, numerical quadrature rules, numerical methods for ODE’s. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS361
Title: Advanced Statistical Modeling
Description: The course will cover the fundamentals of advanced statistical modeling. Topics include: linear and nonlinear regression, goodness of fit tests, generalized linear models, Bayesian inference and hypothesis testing, nonparametric inference and bootstrap. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: MSCIS
Course Code: CS362
Title: Time Series Analysis
Description: This course will provide a systematic account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The topics covered include: difference equations, lag operators, stationary ARMA processes, forecasting, maximum likelihood estimation, spectral analysis, linear regression models, Kalman filter, and Fourier transform methods. Students will apply these methods to solve practical problems in signal processing, statistics, and economics. Three hours of instructor-led class per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS363
Title: Stochastic Models
Description: The course will cover topics including: Conditional Probability and Conditional Expectation, Markov chains, Hidden Markov Models, Markov Chain Monte Carlo methods, introduction to Poisson Processes and Queueing Models. Instructor-led discussions and problem sets.
Credits: 3.0
Prerequisites: CS108
Corequisites:
Program: MSCIS
Course Code: CS364
Title: Game Theory
Description: The course introduces the major concepts and paradigms of game theory, a domain which explores strategic interactions among several players which determine the outcome of the game. Students will explore how to achieve favorable outcomes arising from the modeling, analysis and prediction of player behavior, with a strong focus on the mathematical models of the game dynamics. Game Theory has numerous applications in Economics, Political Science, Social Science, Evolutionary Biology, Computer Science, Engineering, and everyday life situations. Instructor led lecture and discussions; assessment may include problem sets, software implementation, exams, and projects.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS366
Title: Computational Optimization
Description: The course will focus on hard computational problems, relations(reductions) between them as well as theoretical and practical approaches for solving them. We will explore several well-known hard optimization problems such as Traveling Salesman Problem, Knapsack Problem, 3-SAT, various graph problems etc. On example of these problems, we will study topics such as: Problem Complexity classes P, EXP, R and undecidable problems; Cook-Levin Theorem and NP-completeness; various approaches for solving hard problems such as approximation, heuristics, branch and bound and randomization methods; Flow Networks; Integer Linear Programming. The subject of the course falls in the intersection of Computer Science and Mathematics. We will cover some aspects related to data structures, but we will not cover topics related to Machine Learning or Data Science. The intended audience of the course consists of students interested in advanced topics in optimization, combinatorics or discrete mathematics.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS370
Title: Programming Paradigms
Description: The course will cover key principles and structures related to programming. Topics include design patterns, generic programming, an overview of the C++ Standard Template Library, functional programming, logic programming, with examples and implementation using different programming languages to illustrate uses and functionality of different paradigms which are explored. Instructor led lecture and discussions; assessment may include problem sets, programming design projects and software implementation, and examinations.
Credits: 3.0
Prerequisites: CS312
Corequisites:
Program: MSCIS
Course Code: CS371
Title: Image Processing
Description: This course is an introduction to digital image processing. The course covers topics including: image analysis, Image representation and storage, image de-noising and restoration, compression techniques, two-dimensional discrete Fourier transform, spatial and frequency domain, linear and nonlinear image filtering, edge detection, image segmentation, and basics of digital video processing. Graduate students are expected to complete an individual or group project during the semester. Three hours of instructor-led class time per week including discussions and problem sets.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS375
Title: Information Visualizations
Description: Transforming data into knowledge is a multi-step process which can include data cleanup, exploring the relationships between datasets, interpretation, and demonstrating the results using graphics, interactive tools and online dashboards. The course will include hands-on sessions using open source software for the rapid crafting of visualization of many different data types. Students will also analyze large datasets to discover patterns and structures and derive insight into large volumes of data. Through the development of visualization techniques and tools, students will be better positioned to comprehend and convey insights. Instructor-led class discussions with assessment based on participation, problem sets, projects, and exams.
Credits: 3.0
Prerequisites: CS121 or equivalent
Corequisites:
Program: MSCIS
Course Code: CS380
Title: Reinforcement Learning
Description: Reinforcement Learning (RL) refers to a collection of machine learning techniques that solve sequential decision-making problems using a process of trial and error. It is a core area of research in artificial intelligence and machine learning. This course teaches students the key concepts underlying classic and modern algorithms in RL. Students are introduced to statistical learning techniques where an agent explicitly takes actions and interacts with the world. The course covers model-based and model-free (such as Policy Gradient, and Q-learning) algorithms.
Credits: 3.0
Prerequisites: CS340
Corequisites:
Program: MSCIS
Course Code: CS381
Title: Generative AI
Description: Generative models represent a significant leap forward in AI’s capability to create realistic text, images, and other types of data, opening up a plethora of possibilities across various industries, from healthcare and entertainment to manufacturing and beyond. Equipping students with a comprehensive understanding of these models prepares them for cutting-edge roles and innovative projects in their future careers. Students learn not only the technical skills to implement and optimize these models but also the critical thinking required to navigate the ethical considerations associated with them.
Credits: 3.0
Prerequisites: CS340
Corequisites:
Program: MSCIS
Course Code: CS390
Title: Capstone Practicum
Description: Students will complete an 8-12 hour per week industry work experience in a computer-related position. Students will be supervised by assigned personnel at the field site and/or by a program-based instructor. Hours are arranged by mutual consent of the student and employer. Students are required to report periodically to the course instructor, maintain a log of on-the-job activities, and submit a final report regarding the practicum experience. No additional class time is required.
Credits: 3.0
Prerequisites: CS395
Corequisites:
Program: MSCIS
Course Code: CS391
Title: Independent Study
Description: Special study of a particular problem under the direction of a faculty member. The student must present a written, detailed report of the work accomplished. Approval of the CIS Program Chair and the instructor is required.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS392
Title: Special Topics in Computer Science
Description: This course explores topics in applied computer science with emphasis on current technologies, theories, and approaches.The spring 2020 course will investigate the field of distributed algorithms. Students will be introduced to the necessary background on NP-completeness and approximation algorithms. The core of the material will consist of distributed algorithms and impossibility results for different network models. Known classical distributed algorithms will be presented based on the current research papers from prominent conferences in the field. This will include introducing the notion of synchronous/asynchronous algorithms, randomized algorithms, self-stabilization and understanding of these topics under different network constraints. Lectures, readings, and discussions led by instructor with assessment by projects, problems sets, and exams.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS395
Title: Capstone Preparation
Description: The course is designed to prepare students to work on their Master’s capstone. Students will learn of prospective research thesis topics, do literature surveys which will become part of their final capstone report, select their supervisor, and submit an approved capstone proposal. Topics covered will include research methodology in computer science, plagiarism and academic integrity, basics on how to write a technical paper, give a technical talk, search for a job, write a CV and cover letter, interview skills. Instructor-led discussions and presentations.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: CS396
Title: Capstone-Thesis Writing
Description: Students will complete an individual thesis which serves as part of the capstone requirement for the degree. The thesis proposal is presented as part of the CS395 requirements and must be approved by the supervisory committee. Upon completion, the capstone thesis must be successfully presented to the program in an open forum and be approved by the supervisory committee.
Credits: 3.0
Prerequisites: CS395
Corequisites:
Program: MSCIS
Course Code: DS330
Title: Deep Learning
Description: This course provides foundational knowledge in Deep Learning, one of the highly demanded skills in AI. Application of Deep Learning algorithms transform fields such as computer vision, speech recognition, natural language processing, medical image analysis, drug design, audio recognition. Students will be introduced to various state-of-the-art Neural Network architectures (eg DNNs, CNNs, RNNs, LSTMs, GANs) and techniques (eg Stochastic Gradient Descent, Dropout, Batch norm, Transfer Learning). Students will work on real-life datasets to implement techniques applicable in domains such as image recognition, autonomous driving, gaming, healthcare, fraud detection. Instructor-led discussion, along with reading, written, and practical assignments. Assessment via exams and projects.
Credits: 3.0
Prerequisites:
Corequisites:
Program: MSCIS
Course Code: DS335
Title: Generative AI
Description: Generative models represent a significant leap forward in AI’s capability to create realistic text, images, and other types of data, opening up a plethora of possibilities across various industries, from healthcare and entertainment to manufacturing and beyond.
Equipping students with a comprehensive understanding of these models will prepare them for cutting-edge roles and innovative projects in their future careers. They will learn not only the technical skills to implement and optimize these models but also the critical thinking required to navigate the ethical considerations associated with them.
Credits: 3.0
Prerequisites: CS340
Corequisites:
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