Course Descriptions

Zaven & Sonia Akian College of Science and Engineering (CSE) Course Descriptions

CIS060

Discrete Mathematics

Credits:3

Prerequisite:

CIS201

Foundations of CIS – Data Structures and Algorithms

Credits:4

Prerequisite:

CIS265

Software Project Management

Credits:4

Prerequisite:

CIS290

Theory of Computing

Credits:4

Prerequisite:

CIS310

Theory of Computing

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

Prerequisite:

CIS311

Theory of Algorithms

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

Prerequisite:

CIS312

Object-Oriented Analysis and Design

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 (Computer-Assisted Software Engineering) tools to aid in analysis and design.

Credits:3

Prerequisite:

CIS315

Cryptography

Credits:3

Prerequisite:

CIS320

Data Structures and Algorithms

This is a foundation course that prepares for all subsequent CIS courses. Students will develop skills in design of algorithms and efficient implementation of Java programs for creating and processing data structures. The course covers principles of object-oriented programming through in-depth discussion of linear and non-linear data structures, such as linked lists, stacks, queues, trees, tables and graphs. The pivotal topics include recursion, dynamic memory management, searching and sorting algorithms. The course also focuses on further development of Java programming skills, including GUI components, exception handling, generic classes, collections, multithreading and basics of networking.

Credits:3

Prerequisite:

CIS324

Internet Application, Design and Development

Issues in application design specific to Internet hardware, software and users. Students will develop a variety of projects and a final project. Topics will include HTML integration, CGI programming, XML, Java servlets, internationalization issues, client-server and database connectivity.

Credits:3

Prerequisite:

CIS326

Database Systems

Relational query languages. Semantic data models. Logical and physical database design. Privacy issues. Implementation techniques (catalogs, query optimization, concurrency control, security and integrity enforcement)

Credits:3

Prerequisite:

CIS331

Operating Systems

Credits:3

Prerequisite:

CIS350

Software Project Management

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

Prerequisite:

CIS355

Entrepreneurship

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, in-class exercises, readings, guest speakers, and an outside project.

Credits:3

Prerequisite:

CIS395

Capstone Preparation in CIS

Credits:3

Prerequisite:

CIS396

Capstone – Thesis Writing

Credits:3

Prerequisite:

CIS399

Independent Study in CIS

Credits:3

Prerequisite:

CIS600

Graduate Continuing Enrollment

Credits:1

Prerequisite:

CS050

Intro to Java and C++ Programming

Credits:3

Prerequisite:

CS100

Calculus 1

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

Prerequisite:

CS101

Calculus 2

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

Prerequisite:

EQCALC1ENG

CS102

Calculus 3

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

Prerequisite:

CS101

CS103

Real Analysis

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 instructor-led class time per week including discussions and problem sets.

Credits:3

Prerequisite: CS102

CS104

Linear Algebra

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

Prerequisite:

CS105

Ordinary Differential Equations

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

Prerequisite: CS104

CS107

Probability

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

Prerequisite: CS111

CS108

Statistics

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

Prerequisite:CS107

CS110

Introduction to Computer Science

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

Prerequisite:

CS111

Discrete Mathematics

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

Prerequisite:

CS112

Numerical Analysis

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

Prerequisite: CS101

CS112

Numerical Analysis

Credits:3

Prerequisite: CS104

CS120

Introduction to Object Oriented Programing

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

Prerequisite: CS110

CS121

Data Structures

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

Prerequisite: CS111

CS121

Data Structures

Credits:3

Prerequisite: CS120

CS130

Computer Organization

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

Prerequisite: EQOOP

CS131

Human Computer Interaction (HCI)

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

Prerequisite:

CS132

Theory of Communication Networks

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

Prerequisite: CS121

CS140

Mechanics

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

Prerequisite: CS101

CS201

Complex Analysis

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

Prerequisite:

CS202

Functional Analysis

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

Prerequisite: CS103

CS205

Partial Differential Equations

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

Prerequisite: CS105

CS211

Introduction to Algorithms

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

Prerequisite: CS121

CS213

Optimization

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

Prerequisite: CS102, CS112

CS215

Cryptography

Credits:3

Prerequisite:

CS211

CS217

Computer Graphics

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

Prerequisite: CS102, CS221

CS218

Game Development

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

Prerequisite:

CS121

CS219

Mobile Application Development

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.

Credits:3

Prerequisite: EQDATASTRC

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.

CS220

Parallel and High Performance Computing (Parallel HPC)

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

Prerequisite: CS211

CS221

Distributed Systems

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

Prerequisite: CS211

CS222

Database Systems

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

Prerequisite: CS121

CS226

Math Modeling Applications

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

Prerequisite: CS105

CS230

Software Testing Fundamentals

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

Prerequisite: CS120

CS231

Quantum Computing

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

Prerequisite: EQMECH1

CS232

Cybersecurity

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

Prerequisite:

CS236

Compiler Design

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

Prerequisite:

CS130

CS240

Mechanics

The course surveys a range of topics including: the principles of relativity and determinacy, the Galilean group, Newton’s equations; systems with one and two degrees of freedom, conservative force fields, angular momentum, dynamics of a system of n points, the method of similarity; generalized coordinates, variational principles, Lagrange’s equations; conservation laws; integrations of the equations of motions, the two-body central-force problem; collisions between particles; small oscillations; rigid bodies; Hamilton’s equations; Poisson brackets, canonical and non-canonical transformations; the Hamilton-Jacobi equation, adiabatic invariants; canonical perturbation theory. Students are required to develop expertise in the application domain of mechanics. Students will complete individual research projects in order to develop advanced proficiency in discovering and analyzing new material. Three hours of instructor-led class time per week including discussions and problem sets.

Credits:3

Prerequisite:

CS241

Dynamical System

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

Prerequisite:

CS245

Bioinformatics

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

Prerequisite:

CS246

Artificial Intelligence

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

Prerequisite:

EQALGORTHM

CS251

Machine Learning

Machine learning links together computers and statistics by teaching machines to act without human interaction. It compiles those methods of data science that automate model building process for computer realization by applying algorithms that iteratively learn from data allowing computers to find hidden insights in data without explicit programming. This course will provide the basic ideas and methods of machine learning. Topics include – supervised learning, unsupervised learning, best practices in machine learning with many examples from real-world applications. It also includes explanations on how to use the well-known R software for application of the learned techniques to practical problems. Instructor-led discussions and problem sets.

Credits:3

Prerequisite:

CS252

Data Science

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

Prerequisite: CS121

CS260

Image Processing

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

Prerequisite:CS104, CS112, CS121, CS211

CS261

Portfolio Theory and Risk Management

Credits:3

Prerequisite:

CS262

Game Theory

Credits:3

Prerequisite:

CS290

Special Topics in Applied Computer Science

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

Prerequisite: CS121

CS296

Capstone

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

Prerequisite:

CS299

Independent Study

Credits:1

Prerequisite:

CS302

Functional Analysis

Credits:3

Prerequisite:

CS103

CS310

Theory of Computing

Credits:3

Prerequisite:

CS311

Theory of Algorithms

Credits:3

Prerequisite:

CS121

CS312

Object-Oriented Analysis and Design

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

Prerequisite:

CS313

Advanced Topics in Algorithms

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

Prerequisite:

CS314

Theory of Communication Networks

Credits:3

Prerequisite:

CS121

CS315

Cryptography

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

Prerequisite:

CS316

Advanced Cryptography

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

Prerequisite:

CS315

CS317

Computer Graphics

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

Prerequisite:

CS318

Advanced Topics in the Theory of Computation

Course Description tailored to course content when offered.

Credits:3

Prerequisite:

CS319

Computer Vision

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

Prerequisite:

CS108

CS320

Data Structures and algorithms

This is a foundation course that prepares for all subsequent CIS courses. Students will develop skills in design of algorithms and efficient implementation of Java programs for creating and processing data structures. The course covers principles of objectoriented programming through indepth discussion of linear and nonlinear data structures, such as linked lists, stacks, queues, trees, tables and graphs. The pivotal topics include recursion, dynamic memory management, searching and sorting algorithms. The course also focuses on further development of Java programming skills, including GUI components, exception handling, generic classes, collections, multithreading and basics of networking.

Credits:3

Prerequisite:

CS322

Software Engineering

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

Prerequisite:

CS323

Advanced Object-Oriented Programming

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

Prerequisite:

CS325

Development of Geo-Collaborative Applications

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

Prerequisite:

CS326

Database Systems

Credits:3

Prerequisite:

CS327

Parallel and High-Performance Computing (Parallel HPC)

Credits:3

Prerequisite:

CS311

CS328

Human Computer Interaction

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

Prerequisite:

CS329

Data Warehousing

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

Prerequisite:

CS330

Computer Organization

Credits:3

Prerequisite:

CS331

Operating Systems

The organization and structure of modern operating systems. System level programming in Windows and Unix Operating Systems.

Credits:3

Prerequisite:

CS332

System Administration

User administration. Operating system installation, tuning and control. Network administration. Security management. Performance tuning and management.

Credits:3

Prerequisite:

CS333

Network Programming

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

Prerequisite:

CS121

CS334

Performance Analysis and Queueing Theory

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

Prerequisite:

CS335

Introduction to EDA

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

Prerequisite:

CS336

Compiler Design

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

Prerequisite:

CS130

CS337

Cybersecurity

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

Prerequisite:

CS338

Distributed Systems

Credits:3

Prerequisite: CS311

CS339

Quantum Computing

Credits:3

Prerequisite:

CS340

Machine Learning

Credits:3

Prerequisite:

CS342

Data Science

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

Prerequisite:

CS343

Data Visualisation

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

Prerequisite:

CS345

Bioinformatics

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

Prerequisite:

CS346

Artificial Intelligence and Decision Support

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

Prerequisite:

CS347

Knowledge Representation

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

Prerequisite:

CS350

Software Project Management

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

Prerequisite:

CS355

Entrepreneurship

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

Prerequisite:

CS360

Computational Methods

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

Prerequisite:

CS361

Advanced Statistical Modeling

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

Prerequisite:

CS108

CS362

Time Series Analysis

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

Prerequisite:

CS363

Stochastic Models

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

Prerequisite: CS108

CS364

Game Theory

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

Prerequisite: CS107

CS370

Programming Paradigms

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

Prerequisite: CS312

CS371

Image Processing

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

Prerequisite:

CS375

Information Visualizations

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

Prerequisite: CS121

CS390

Capstone Practicum

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

Prerequisite: CS395

CS391

Independent Study

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

Prerequisite:

CS392

Special Topics in Computer Science

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

Prerequisite:

CS392CC

Special Topics in Computer Science: Cloud Computing

Credits:3

Prerequisite:

CS395

Capstone Preparation

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

Prerequisite:

CS396

Capstone-Thesis Writing

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

Prerequisite: CS395

CSE184

TBD

Credits:3

Prerequisite:

DS110

Statistics 2

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

Prerequisite: CS108

DS115

Data Structures/Algorithms in Data Science

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

Prerequisite: CS111

DS115

Data Structures/Algorithms in Data Science

Credits:4

Prerequisite: DS120

DS116

Data Visualization

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

Prerequisite: CS108

DS120

Programming for Data Science

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

Prerequisite: CS110

DS150

Physics and Chemistry in Life Sciences

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

Prerequisite:

DS151

Cell and Molecular Biology

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

Prerequisite:

DS205

Databases & Distributed Systems

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

Prerequisite: DS115

DS206

Business Intelligence

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

Prerequisite: DS205

DS207

Time Series Forecasting

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

Prerequisite:

CS108

DS209

Spatial Data Science

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

Prerequisite: CS108

DS211

Introduction to Bioinformatics

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

Prerequisite:

DS213

Computational Biology

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

Prerequisite: CS251

DS213

Computational Biology

Credits:3

Prerequisite: DS211

DS215

Networks and System Biology

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

Prerequisite:

DS216

Cheminformatics

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

Prerequisite: CS108, DS115, DS150

DS217

Biostatistics

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

Prerequisite: CS108

DS219

Causal Inference

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

Prerequisite: CS108

DS221

Urban Data Science

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

Prerequisite: CS108, ENGS103

DS223

Marketing Analytics

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

Prerequisite: CS108

DS225

Applications of Machine Learning in Natural and Life Sciences

Students will learn about applications of machine learning (ML) and deep learning algorithms for solving open problems in natural and life sciences. Students will study recently published research papers and, amongst others, learn about the data-driven discovery of partial differential equations through sparse regression and protein structure prediction from its sequence through deep learning. Students will also aim to reproduce and use results from these papers. Knowledge of ML algorithms is required.

Credits:3

Prerequisite: CS251

DS226

Bayesian Statistics

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

Prerequisite: CS108

DS227

Business Analytics for Data Science

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

Prerequisite: BUS101

DS227

Business Analytics for Data Science

Credits:3

Prerequisite: DS205

DS228

Product Management

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

Prerequisite:

DS229

Machine Learning Operations (MLOps)

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

Prerequisite: CS251

DS231

Computer Vision

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

Prerequisite: CS251

DS233

Natural Language Processing

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

Prerequisite: CS251

DS244

Biomedical Imaging and Cell Staining

Credits:3

Prerequisite:

DS250

Managerial Accounting and Analysis

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

Prerequisite: BUS101

DS277

Biomedical Imaging and Cell Staining

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

Prerequisite:

DS299

Capstone

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

Prerequisite:

DS330

Deep Learning

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

Prerequisite:

DS343

Data Visualization

Credits:3

Prerequisite:

ENGS101

Calculus: Single Variable

This introductory calculus course for engineering students covers differentiation and integration of functions of one variable, with applications. Topics include Concepts of Function, Limits and Continuity, Differentiation Rules, Application to Graphing, Rates, Approximations, and Extremum Problems, Definite and Indefinite Integration, The Fundamental Theorem of Calculus, Applications to Geometry: Area, Volume, and Arc Length, Applications to Science: Average Values, Work, and Probability, Techniques of Integration, and Approximation of Definite Integrals, Improper Integrals, and L’Hôspital’s Rule. Instructor-led class time including problem sets and discussions.

Credits:4

Prerequisite:

ENGS102

Calculus: Multi Variable

This calculus course builds on topics covered in Calculus: Single Variable, encompassing vector and multi-variable calculus. Topics include power series and their expansions, partial differentiation and multiple integration with applications, vectors, and vector-valued functions. Line and surface integrals are introduced along with their application to concepts of work and flux, and studied by means of the theorems of Green, Gauss, and Stokes. Instructor-led class time including problem sets and discussions.

Credits:4

Prerequisite: ENGS101

ENGS103

Linear Algebra and Ordinary Differential Equations

This course introduces students to linear algebra and ordinary differential equations (ODEs), including general numerical approaches to solving systems of equations. Topics include linear systems of equations, existence and uniqueness of solutions, Gaussian elimination, initial value problems, 1st and 2nd order systems, forward and backward Euler, and the Runge-Kutta method (RK4). The course also covers eigenproblems: eigenvalues and eigenvectors, including complex numbers, functions, vectors and matrices. Instructor-led class time including problem sets and discussions.

Credits:4

Prerequisite:

ENGS104

Probability and Statistics

Credits:3

Prerequisite: CS104

ENGS110

Introduction to Programming

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

Prerequisite:

ENGS115

Data Structures and Algorithms

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. Instructor-led class time including problem sets and discussions.

Credits:3

Prerequisite: CS111

ENGS115

Data Structures and Algorithms

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. Instructor-led class time including problem sets and discussions.

Credits:3

Prerequisite: ENGS110

ENGS121

Mechanics

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

Prerequisite: CS101

ENGS122

Mechanics Lab

Hands-on laboratory course to accompany Mechanics. Students will conduct experiments in support of the topics covered in Mechanics.

Credits:1

Prerequisite:

ENGS123

Electricity and Magnetism

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

Prerequisite: EQCALC1ENG

ENGS124

Electricity and Magnetism Lab

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

Prerequisite:

ENGS132

Chemistry Lab

Hands-on laboratory course to accompany Chemistry. Students will conduct experiments in support of the topics covered in Chemistry.

Credits:1

Prerequisite:

ENGS135

Introduction to Chemical Engineering

Credits:3

Prerequisite:

ENGS141

Engineering Statics

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

Prerequisite: ENGS121

ENGS142

Engineering Dynamics

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

Prerequisite: ENGS141

ENGS151

Circuits

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

Prerequisite: ENGS123

ENGS152

Circuits Lab

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.

ENGS171

Biology

Credits:3

Prerequisite:

ENGS174

Biotechnology

Credits:3

Prerequisite:

ENGS176

Environmental Engineering

Credits:3

Prerequisite:

ENGS181

Introduction to Materials Science

Credits:3

Prerequisite:

ENGS211

Numerical Methods

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

Prerequisite: CS104

ENGS211

Numerical Methods

Credits:3

Prerequisite: ENGS103

ENGS230

Introduction to Quantum Computing

Credits:3

Prerequisite: EQMECH1

ENGS241

Computer-Aided Design

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

Prerequisite: ENGS141

ENGS245

Thermodynamics

Credits:3

Prerequisite:

ENGS246

Heat Transfer

Credits:3

Prerequisite:

ENGS248

Introduction to Fluid Mechanics

Credits:3

Prerequisite:

ENGS251

Embedded Systems

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

Prerequisite: ENGS151

ENGS252

Signals and Systems

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

Prerequisite: ENGS142

ENGS252

Signals and Systems

Credits:3

Prerequisite: ENGS151

ENGS253

Embedded systems Lab

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

Prerequisite:

ENGS261

Control Systems 1

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

Prerequisite: ENGS252

ENGS262

Control Systems Lab

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

Prerequisite:

ENGS263

Control Systems 2

Building on Control Systems 1, this course engages students in more rigorous analysis in control theory. Methods include time domain modeling, trajectories and phase plane analysis, similarity transforms, controllability and observability, pole placement and observers, linear quadratic optimal control, Lyapunov stability and describing functions and simulation. This course is intended to be taken concurrently with Control Systems 2 Lab. Instructor-led class time including problem sets as well as experimentation in a variety of controls applications.

Credits:3

Prerequisite:

ENGS264

Control Systems 2 Lab

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

Prerequisite:

ENGS265

Mechatronics Design

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

Prerequisite: ENGS241

ENGS271

Systems Engineering

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

Prerequisite:

ENGS275

Resource Management

Credits:3

Prerequisite:

ENGS276

Project Management

Credits:3

Prerequisite:

ENGS280

Alternative Energy

Credits:3

Prerequisite:

ENGS290

Special Topics: UAV Modeling

This course focuses on the design cycle for Unmanned Aerial Vehicles (UAVs). The course will introduce training on flight simulators and field flights, as well as pre-flight and post-flight checklist preparation and application. Students will focus on conceptual design and in-flight validation, with some exposure to modeling, simulation, identification and control. Assessment may include problem sets, exams, and in-the-field projects.

Credits:1

Prerequisite:

ENGS298

Capstone 1

Credits:3

Prerequisite:

ENGS299

Capstone 2

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

Prerequisite: ENGS298

ENV102

Modes of Enquiry in ENV and Sustainability

Credits:3

Prerequisite:

ENV103

Biology and Ecosystems

Credits:3

Prerequisite:

ENV104

Biology Lab

Credits:1

Prerequisite:

ENV106

Solid Waste in Circular Economy(project based)

Credits:3

Prerequisite:

ENV107

Sustainable Food Systems(project based)

Credits:3

Prerequisite:

ENV108

Chemistry for Environment and Sustainability or Alternative

Credits:3

Prerequisite:

ENV109

Chemistry Lab

Credits:1

Prerequisite:

ENV111

Statistics

Credits:3

Prerequisite:

ENV162

Environment and Sustainability Assessment Tools

Credits:3

Prerequisite:

ENV205

Environmental Monitoring Lab/Field

Credits:1

Prerequisite:

ENV213

Scientific Method in Environment and Sustainability

Credits:1

Prerequisite:

ENV214

Climate Science and Politics

Credits:3

Prerequisite:

ENV216

Circular Economy+ Bioeconomy

Credits:3

Prerequisite:

ENV217

Environmental and Natural Resource Economics

Credits:3

Prerequisite:

ENV218

Environmental and Sustainable Governance

Credits:3

Prerequisite:

ENV219

Environmental and Sustainable Modeling

Credits:3

Prerequisite:

ENV222

Mobility and Transport Planning

Credits:3

Prerequisite:

ENV223

Earth Sciences

Credits:3

Prerequisite:

ENV224

Environmental Geology Lab

Credits:3

Prerequisite:

ENV226

Resilemce Planning and Management

Credits:3

Prerequisite:

ENV227

Internship/Fieldwork

Credits:3

Prerequisite:

ENV235

Capstone

Credits:3

Prerequisite:

EPIC331

Translations: Digital Fabrication in Design

The course aims to partake in the ongoing discourse about the role of fabrication for the production of design and manufacture. More specifically it aspires to draw a connection between the advancement of design ideas and the use of techniques specific to the digital fabrication pipeline, and to posit how this coupling may further inform other areas of disciplinary research. As such, the course aims to be a research laboratory for the analysis, development and localized deployment of strategies for digital fabrication. Topics of study include: design/fabrication typologies, technical performance, sustainable strategies, prefabrication methodology, current and future developments in design/fabrication, among others.

Credits:3

Prerequisite:

ESS101

Introduction to Environmental and Sustainability Sciences

Credits:3

Prerequisite:

ESS102

Modes of Inquiry in ESS

Credits:3

Prerequisite:

IE300

Probability Theory

Credits:3

Prerequisite:

IE340

Engineering Economics

Credits:3

Prerequisite:

IE349

Enabling Competitive Advantage through Information Technology

Credits:3

Prerequisite:

IE350

Alternative Energy

Credits:3

Prerequisite:

IESM050

Intro to MATLAB

Three hours of lecture per week. MATLAB (MATrix LABoratory) is a leading software used for numerical analysis. It provides an environment for computation and visualization. Students will work toward developing a working knowledge of MATLAB to implement and test algorithms, thus enabling a deeper understanding of and facility working with analytical engineering tools.

Credits:3

Prerequisite:

IESM106

Probability and Statistics

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

Prerequisite:

IESM220

Operation Research 1

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

Prerequisite:

IESM300

Probability Theory

This course is an introduction to the mathematical study of randomness and uncertainty. Axioms of probability, conditional probability and independence, combinatorial analysis and application, discrete and continuous random variables, expectation, variance and covariance, transformation of random variables, moment generating functions, characteristic functions, limit theorems, selected probability models, binomial, polynomial, Poisson, hypergeometric, normal, uniform, exponential, lognormal and gamma distributions, simulations, bivariate normal vector, the simplest time‐dependent stochastic processes, Markov chains, Poisson process, the Brownian motion, the Black‐Scholes option pricing formula, engineering applications.

Credits:3

Prerequisite:

IESM301

Analysis and Design of Data Systems

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

Prerequisite:

IESM308

Simulation of Industrial Engineering Systems

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

Prerequisite:

IESM310

Engineering Statistics

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. 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

Prerequisite:

IESM311

Quality Assurance and Management

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

Prerequisite:

IESM313

Data Mining & Predictive Analytics

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

Prerequisite:

IESM315

Design and Analysis of Experiments

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

Prerequisite:

IESM320

Operations Research 1

Deterministic linear optimization models and applications: linear programming, duality, postoptimality (sensitivity and parametric) analysis, formulation of linear programs, optimal allocation and control problems in industry and environmental studies, convex sets, properties of optimal solutions, simplex and revised simplex algorithms, problems with special structures, e.g., transportation and assignment problems, network problems.

Credits:3

Prerequisite:

IESM321

Operations Research 2

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

Prerequisite: IESM220

IESM324

Applied Statistics for Engineers

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

Prerequisite:

IESM325

Decision Analysis

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

Prerequisite:

IESM330

Simulation of Industrial Engineering Systems

Credits:3

Prerequisite:

IESM331

Production Systems analysis

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

Prerequisite:

IESM335

Facilities Planning and Design

Three hours of lecture per week. Modeling and design of plant layout and balancing of conveyor systems; activity relationships and space requirements; analysis of integrated materials control systems involving functions of storing, recalling, delivery, inventory, and computer control; design and evaluation of automated warehousing and order-picking systems.

Credits:3

Prerequisite:

IESM339

Production and Operation Management

This course will introduce concepts and techniques for design, planning and control of manufacturing and service operations. It was created in collaboration with the MIT Sloan of Management course, Operations Management. The course provides basic definitions of operations management terms; tools and techniques for analyzing operations; and strategic context for making operational decisions. It incorporates HBS cases and HBR articles. The material is presented in six modules:

Credits:3

Prerequisite:

IESM340

Engineering Economics

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

Prerequisite:

IESM341

Introduction to Management

An examination of the inter-relationships of structure, operations, and management processes in modern organizations. The basic functions of Western management, including their application to managing in Armenia’s changing organizations. Emphasis will be placed on acquiring knowledge and skills necessary for the effective practice of management.

Credits:3

Prerequisite:

IESM342

Microeconomics

The course is to introduce students the fundamentals of economics, with particular focus on microeconomics. Engineers are responsible for designs of products and systems that are not only technically feasible but also economically viable. In particular, industrial engineers are often responsible for initiating major investments; e.g., should we introduce a new product or build a new manufacturing plant? Such decisions require taking into account many factors, including the time value of money, taxation, estimation and risk analysis. At the same time they require adequate knowledge and understanding of the surrounding economic environment, i.e. market forces and economic factors that affect business decisions; factors behind Government policies and their possible implications for businesses.

Credits:3

Prerequisite:

IESM345

Supply Chain Management

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

Prerequisite:

IESM346

Managing Engineering and Technology

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

Prerequisite:

IESM347

Design and Innovation of Information Services

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

Prerequisite:

IESM349

Enabling Competitive Advantage through Information Technology

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

Prerequisite:

IESM350

Alternative Energy

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

Prerequisite:

IESM351

Sustainable Smart and Resource Efficient Systems 1: Systems and Technologies

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

Prerequisite:

IESM352

Sustainable Smart and Resource Efficient Systems 2: Decision Making Tools

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

Prerequisite:

IESM360

Computer-Aided Design

Fundamentals of part design; computer-aided design tools and data structures; geometric modeling; transformations; CAD/CAM data exchange; mechanical assembly.

Credits:3

Prerequisite:

IESM361

Computer-Aided Manufacturing

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

Prerequisite:

IESM362

Advanced CAD/CAM Applications

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

Prerequisite:

IESM371

Econometrics

Credits:3

Prerequisite:

IESM372

Portfolio Theory and Risk Management

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

Prerequisite: EQOptOR

IESM372

Portfolio Theory and Risk Management

Credits:3

Prerequisite: EQStatPro

IESM390

Integrative Project in Modern Production Methods

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

Prerequisite:

IESM391

Independent Study

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

Prerequisite:

IESM395

Capstone Preparation

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

Prerequisite:

IESM396

Capstone: Thesis

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

Prerequisite: IESM395

IESM397

Capstone: Project

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

Prerequisite: IESM395

IESM600

Graduate Continuing Enrollment

Credits:1

Prerequisite:

STAT110

Applied Statistics

This course introduces the necessary core quantitative methods that will be needed in future offerings as part of the BA in Business program. Statistical software and the use of spreadsheets are integrated throughout so that students better comprehend the importance of using modern technological tools for effective model building and decision-making. The course will make use of a data-oriented approach in exposing students to basic statistical methods, their conceptual underpinning, such as variability and uncertainty, and their use in the real world. Topics include data collection, descriptive statistics, elementary probability rules and distributions, sampling distributions, and basic inference. The course will also cover selected non-statistical quantitative techniques applied to business models, including curve fitting, optimization, and introduction to the time value of money.

Credits:3

Prerequisite: