CS 050 Intro to Java and C++ Programming (Credits: 3)
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CS 100 Calculus I (Credits: 3)
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.
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CS 101 Calculus 2 (Credits: 3)
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.
Prerequisites: CS100
Corequisites:
CS 102 Calculus 3 (Credits: 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.
Prerequisites: CS101 CS104
Corequisites:
CS 103 Real Analysis (Credits: 3)
The fundamental concepts in analysis are rigorously treated with emphasis on reasoning and proofs. The topics include completeness and order properties of real numbers, limits, continuity and uniform continuity, conditions for integrability and differentiability, infinite sequences and series, basic concepts of topology and measure, metric spaces, compactness, connectedness, continuous functions on a compact set, the contraction mapping lemma. Students are required to apply practical analytical methods to formulate, critically assess, and solve problems which arise in computational sciences and mathematical modeling. Three hours of instructorled class time per week including discussions and problem sets.
Prerequisites: CS102
Corequisites:
CS 104 Linear Algebra (Credits: 3)
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.
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CS 105 Ordinary Differential Equations (Credits: 3)
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.
Prerequisites: CS101 CS104
Corequisites:
CS 106 Probability & Statistics (Credits: 3Course will not be offered again)
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.
Prerequisites: CS101 CS111
Corequisites:
CS 107 Probability (Credits: 3)
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.
Prerequisites: CS101 CS111
Corequisites:
CS 108 Statistics* (Credits: 3)
To be submitted next term
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CS 110 Introduction to Computer Science (Credits: 3)
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.
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CS 111 Discrete Mathematics (Credits: 3)
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. Three hours of instructor-led class time per week including discussions and problem sets.
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CS 112 Numerical Analysis (Credits: 3)
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.
Prerequisites: CS101 CS104
Corequisites:
CS 120 Introduction to Object-Oriented Programming (Credits: 3)
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.
Prerequisites: CS110
Corequisites:
CS 121 Data Structures (Credits: 3)
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.
Prerequisites: CS111 CS120
Corequisites:
CS 130 Computer Organization (Credits: 3)
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.
Prerequisites: CS121
Corequisites:
CS 131 Human Computer Interaction (HCI) (Credits: 3)
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.
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CS 132 Theory of Communication Networks (Credits: 3)
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.
Prerequisites: CS121
Corequisites:
CS 140 Mechanics (Credits: 3)
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.
Prerequisites: CS101
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CS 201 Complex Analysis (Credits: 3)
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.
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CS 205 Partial Differential Equations (Credits: 3)
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.
Prerequisites: CS105
Corequisites:
CS 211 Introduction to Algorithms (Credits: 3)
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.
Prerequisites: CS121
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CS 213 Optimization (Credits: 3)
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.
Prerequisites: CS102 CS112
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CS 215 Cryptography (Credits: 3)
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.
Prerequisites: CS211
Corequisites:
CS 220 Parallel and High Performance Computing (Parallel HPC) (Credits: 3)
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.
Prerequisites: CS211
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CS 221 Distributed Systems (Credits: 3)
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.
Prerequisites: CS211
Corequisites:
CS 222 Database Systems (Credits: 3)
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.
Prerequisites: CS211
Corequisites:
CS 240 Mechanics (Credits: 3)
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.
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CS 241 Dynamical System (Credits: 3)
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.
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CS 245 Bioinformatics (Credits: 3)
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.
Prerequisites: CS211
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CS 246 Artificial Intelligence (AI) (Credits: 3)
The course covers topics including: basic artificial intelligence concepts; Bayes networks; hidden Markov models; supervised machine learning, unsupervised machine learning, reinforcement learning; games; image processing, computer vision, robot motion planning; natural language processing. Students are required to complete models and projects in order to develop intermediate level expertise in the application domain of artificial intelligence, and associated knowledge and experience in the contemporary computing technology of AI. Through coursework, students will complete and present their independent research to a broad audience. Three hours of instructor-led class time per week including discussions and problem sets.
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CS 251 Machine Learning (Credits: 3)
Prerequisites: CS 108 OR
CS 106*
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CS 296 Capstone (Credits: 3)
Students will select a topic from their respective tracks and work on the course-long project under the mentorship of the advising instructor. No more than ten students will be mentored by one advisor. Students will discuss each others’ 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. Three hours of instructor-led class time per week.
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CS 310 Theory of Computing (Credits: 3)
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.
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CS 311 Theory of Algorithms (Credits: 3)
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.
Prerequisites: CS121
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CS 312 Object-Oriented Analysis and Design (Credits: 3)
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.
Prerequisites: CS121
Corequisites:
CS 313 Advanced Topics in Algorithms (Credits: 3)
Course Description tailored to course content when offered.
Prerequisites: CS311
Corequisites:
CS 314 Theory of Communication Networks (Credits: 3)
This 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. Some students may complete programming projects by implementing and comparing performances of different communication algorithms. Three hours of instructor-led class time per week including discussions and problem sets.
Prerequisites: CS121
Corequisites:
CS 315 Cryptography (Credits: 3)
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.
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CS 318 Advanced Topics in the Theory of Computation (Credits: 3)
Course Description tailored to course content when offered.
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CS 322 Software Engineering (Credits: 3)
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.
Prerequisites: CS121
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CS 323 Advanced Object-Oriented Programming (Credits: 3)
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..
Prerequisites: CS121
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CS 325 Development of Geo-Collaborative Applications (Credits: 2)
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CS 326 Database Systems (Credits: 3)
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.
Prerequisites: CS311
Corequisites:
CS 327 Parallel and High-Performance Computing (Parallel HPC) (Credits: 3)
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.
Prerequisites: CS311
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CS 331 Operating Systems (Credits: 3)
The organization and structure of modern operating systems. System level programming in Windows and Unix Operating Systems.
Prerequisites: CIS320 CS330
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CS 332 System Administration (Credits: 3)
User administration. Operating system installation, tuning and control. Network administration. Security management. Performance tuning and management.
Prerequisites: CS331
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CS 333 Network Programming (Credits: 3)
Prerequisites: CS121
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CS 334 Performance Analysis and Queueing Theory (Credits: 3)
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.
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CS 335 Introduction to EDA (Credits: 3)
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.
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CS 338 Distributed Systems (Credits: 3)
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.
Prerequisites: CS311
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CS 340 Machine Learning (Credits: 3)
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. Three hours of instructor-led class time per week including discussions and problem sets.
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CS 345 Bioinformatics (Credits: 3)
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.
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CS 350 Software Project Management (Credits: 3)
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.
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CS 355 Entrepreneurship (Credits: 3)
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.
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CS 390 Capstone Practicum (Credits: 3)
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CS 391 Independent Study (Credits: 3)
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.
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CS 395 Capstone Preparation (Credits: 3)
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CS 396 Capstone-Thesis Writing (Credits: 3)
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CSE 111 The Scientific Method and Critical Thinking (Credits: 3)
Science and technology proficiency is indispensable for functioning in modern societies. We are overwhelmed with instant information in all sensory formats and we must be able to discriminate between facts and fallacies, while recognizing our own underlying biases. In this course, the student is introduced to the basic tenets of the scientific method, critical thinking and illustrated real world examples and case studies, with several general topics examined in depth. Such topics includes: pharmaceutical studies, computer performance claims, climate change, emerging technologies, marketing and advertisement, international relations, political and partisan hyperbole.
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CSE 141 Introduction to Data (Credits: 3)
The goal of the course is to present the basic concepts of data analytics, starting from the basics of descriptive statistics and ending with applications of text mining. Students will learn how the statistics is used to model uncertainty, discover patterns in data and make actionable decisions. Basic methods of statistical inference and predictive modeling will be covered. The models and methods will be applied in different fields such as business, social sciences, health care, sports, etc. We will use open source analytical software R in doing statistical calculations. No prior knowledge in programming or experience with R is necessary for the course. Three hours of instructor-led class time per week.
Prerequisites: CS 100 OR CHSS 183 OR
BUS 110
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CSE 145 Geographic Information Systems (Credits: 3)
This course is meant to introduce students to geographic information systems (GIS) and spatial analysis: setting up, analysing, visualizing, and solving problems using data and maps. With advancements in the information technologies more and more industries are relying on GIS to analyse and visualize data. This course will look at applications of GIS in environmental sciences, public health, sustainable transportation planning, land use mapping, telecommunications, hydrology, meteorology, police dispatching, crime patterns, etc. The course will also look at remote sensing technologies like radar, LiDAR, GPS, and the role they play in collecting and analysing data. Another aim of this course is to spark interest in different types of students: from students interested in learning about GIS, to future professionals in fields regularly using GIS, to data enthusiasts and software developers. Three hours of instructor-led class time per week.
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CSE 151 Introduction to Energy Sources (Credits: 3)
Energy drives the human civilization, and any economic growth or poverty alleviation directly involves use of energy resources. This course serves as an introduction to various sources of energy and the mechanisms to harness and convert them to more useful types of energy. Fossil fueled, solar, hydro and nuclear sources and some of their effects on the environment and safety issues will be discussed. This course fulfills general education requirements of the American University of Armenia. There are no prerequisites for this course beyond basic mathematical skills. Three hours of instructor-led class time per week.
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CSE 162 Introduction to Bioscience and its Impact on Research Business and Society (Credits: 3)
This course introduces students to important concepts, techniques and applications of bioscience, and explores its impact on research, business and society. Students will study basic concepts of molecular and cellular biology, biochemistry, molecular genetics, computational biology and biotechnology. Some important applications of molecular and cellular biology in medicine and industry – such as molecular diagnostics of diseases, stem cell and transplantation, drug design and genetically modified foods – will be introduced. Students will also discuss the political, ethical, and legal issues accompanying these topics and their current and future impact on society. Three hours of instructorled class time per week.
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CSE 165 Introduction to Chemistry (Credits: 3)
This course aims to build knowledge of general chemistry required to understand links between chemical research and health science. Nowadays chemistry helps to solve many problems arising in the world. Chemists frequently get inspiration from living things to design new medications, safer chemical reactions and to solve environmental problems arising from human activities. Students will attend lectures and engage in group work on basic chemistry topics. Students will also engage in literature research and interpretation aiming to develop the skills necessary to read and understand research on toxicology, modern developments in chemistry linked to and/or inspired from living things. At the end of the semester, students will present projects on chemistry and health topics. Three hours of instructor-led class time per week.
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CSE 171 Conceptual Physics (Credits: 3)
This course will explore the basic concepts in physics and physical processes. The conceptual viewpoint taken in the course will focus more on the physical description of the processes and phenomena rather than the detailed mathematical equations that govern them. The course will cover topics in mechanics of moving bodies, heat transfer, propagation of sound, properties of light, electricity and magnetism with special emphasis on everyday experience and practical illustrations taken from real life, e.g. art, music, sports, the home. For each of the processes covered in the course, a brief historical perspective will be given, followed by a description of its physical principles, and finally the basic equations that describe it mathematically. Students will be exposed to real-life applications of the theories discussed in the classroom. Three hours of instructor-led class time per week.
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CSE 175 Relativity (Credits: 3)
The course explains Einstein’s Theory of Relativity without requiring science background. The explanation of the theory demands no prior knowledge of mathematics or physics beyond an ability to do simple arithmetic. The first portion of the course introduces some of the main concepts of the theory and discusses experimental tests by using no more than arithmetic and simple geometry. The further progress requires algebra and more advanced mathematical techniques. The concepts are explained in a way accessible to beginners, i.e. those without a background on physics. Three hours of intruction-led class time per week.
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CSE 181 Creativity and Technological Innovation (Credits: 3)
This course introduces students to creativity and its elements, the creative mind and thinking, techniques, concepts and applications leading to technological innovations. Lectures will provide examples of creative thinking and technological innovations from real life creators and technology innovators whose work is well known. Students will work in groups. Each group will create a technological project attempting to solve a real life need based on the knowledge gained and discussed during the semester. Students will be introduced to various problem-solving techniques. Upon completion of this couse, students will be able to think creatively and they will be familiar with the process of technological innovation and innvention. Three hours of intruction-led class time per week.
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CSE 241 Data Mining (Credits: 3)
The goal of the course is to present the basic concepts of data analytics, starting from the basics of descriptive
statistics and ending with applications of text mining. Students will learn how the statistics is used to model uncertainty, discover patterns in data and make actionable decisions. Basic methods of statistical inference and predictive modeling will be covered. At the end of the class several advanced methods of data mining (boosting trees and neural networks) will be presented. The models and methods will be applied in different fields such as business, social sciences, health care, sports, etc. We will use open source analytical software R in doing statistical calculations. The students will also learn how to participate in world’s leading data mining competitions. No prior knowledge in programming or experience with R is necessary for the course. Three hours of instructor-led class time per week.
Prerequisites: CS 100 OR
CHSS 183 OR BUS 110 OR BUS 109
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CSE 262 Quantitative Biology (Credits: 3)
Biology has long been considered a descriptive science with few components in research methods. Since the discovery of the DNA structure and advances in genetics and biotechnology, biology has evolved into an exact and quantitative science. Today, biology uses tools adapted from statistics, mathematics, big data management systems and high performance computing. This course presents state-of-the-art computational biology, provides hands-on experience with tools and approaches for scientific computing in biology, and discusses current and upcoming challenges of transforming biological data into biological knowledge.
Prerequisites: One lower division course that clusters with
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CSE 285 How Things Work (Credits: 3)
This course introduces students to detailed explanations behind the theory, function, and operation of selected technologies, answering the question, How does that work? This is a course in the physical and technological innovations in everyday life employing a minimum of mathematics. It explores the principles of automobiles, propulsion, digital media, cellular technologies, cyber security, nuclear and solar power generation, computer systems, etc. In-class demonstrations will aid in demystifying many topics. Lectures will look inside products from our daily lives to see what scientific principles make them work, focusing on their principles of operation, histories and relationships to one another. Students will work individually, and additionally, present to the class as a group on an emerging technology. The course will be split into three themes: The Digital World, Power and Energy, and Daily Motion. Three hours of instructor-led class time per week.
Prerequisites: One lower division course that clusters with
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CSE 291 Introduction to Product Design (Credits: 3)
An introduction to 3D design techniques and graphics communication tools necessary for a product design. Students learn 3D modeling, assembling, mechanism design, and simulation tools via Parametric Technology Corporation (PTC) company’s online tutorials and demonstrations. Through number of lectures they learn also basic product design communication tools – drawing standards, units, projection views, dimensioning, sections, etc. The knowledge acquired during the course will help students transform their ideas to Computer-Aided Design 3D models and drawings. Also, they will be prepared to apply these powerful design tools in further more advanced courses and their work practice. The evaluation will be done through PTC Precision Learning portal self-assessment questions, home assignments and product design project.
Prerequisites: One lower division course that clusters with
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IESM 050 Intro to MATLAB (Credits: 3)
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.
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IESM 300 Probability Theory (Credits: 3)
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.
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IESM 301 Analysis and Design of Data Systems (Credits: 3)
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.
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IESM 308 Simulation of Industrial Engineering Systems (Credits: 3)
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.
Prerequisites: IESM310
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IESM 310 Engineering Statistics (Credits: 3)
Three hours of lecture per week. Elements of statistical inference, point and interval estimation, regression and correlation, hypothesis testing, maximum likelihood estimation, Bayesian updating, use of statistical software.
Prerequisites: IESM300
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IESM 311 Quality Assurance and Management (Credits: 3)
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.
Prerequisites: IESM310
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IESM 313 Data Mining & Predictive Analytics (Credits: 3)
Exploratory Data Analysis; Classification: Decision Trees, Model Evaluation, Overfitting; Linear and Logistic Regression; Association Analysis; Cluster Analysis; Anomaly Detection; Model Building and Validation
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IESM 315 Design and Analysis of Experiments (Credits: 3)
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.
Prerequisites: IESM310
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IESM 320 Operations Research 1 (Credits: 3)
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.
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IESM 321 Operations Research 2 (Credits: 3)
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.
Prerequisites: IESM300 IESM320
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IESM 325 Decision Analysis (Credits: 3)
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.
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IESM 330 Simulation of Industrial Engineering Systems (Credits: 3)
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.
Prerequisites: IESM310
Corequisites: IESM321
IESM 331 Production Systems Analysis (Credits: 3)
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.
Prerequisites: IESM310
Corequisites: IESM321
IESM 335 Facilities Planning and Design (Credits: 3)
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.
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IESM 339 Production and Operation Management (Credits: 3)
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:
Prerequisites: IESM310 IESM321
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IESM 340 Engineering Economics (Credits: 3)
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).
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IESM 341 Introduction to Management (Credits: 3)
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.
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IESM 342 Microeconomics (Credits: 3)
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.
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IESM 345 Supply Chain Management (Credits: 3)
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.
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IESM 346 Business Management (Credits: 3)
TBD
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IESM 347 Design and Innovation of Information Services (Credits: 3)
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.
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IESM 349 Enabling Competitive Advantage through Information Technology (Credits: 3)
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.
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IESM 350 Alternative Energy (Credits: 3)
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.
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IESM 351 Engineering Green Buildings (Credits: 3)
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, water, and waste with the aim of estimating ways to achieve “carbon neutrality,” “zero emissions” among other green goals. Students will also be introduced to green building norms established by the US Green Building Council as well as other international comparatives.
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IESM 352 Decision Making Tools for Energy Use and Generation (Credits: 3)
The course reviews: 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.
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IESM 360 Computer-Aided Design (Credits: 3)
Fundamentals of part design; computer-aided design tools and data structures; geometric modeling; transformations; CAD/CAM data exchange; mechanical assembly.
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IESM 361 Computer-Aided Manufacturing (Credits: 3)
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.
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IESM 362 Advanced CAD/CAM Applications (Credits: 3)
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.
Prerequisites: IESM 360 OR IESM361
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IESM 371 Econometrics (Credits: 3)
TBD
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IESM 390 Integrative Project in Modern Production Methods (Credits: 3)
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.
Prerequisites: IESM310 IESM320 IESM395
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IESM 391 Independent Study (Credits: 3)
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.
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IESM 395 Capstone Preparation (Credits: 3)
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.
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IESM 396 Capstone: Thesis (Credits: 4)
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.
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IESM 397 Capstone: Project (Credits: 1)
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.
Prerequisites: IESM395
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