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.
Corequisite:
Prerequisite:
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.
Corequisite:
Prerequisite: CS100
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.
Corequisite:
Prerequisite: CS101 CS104
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.
Corequisite:
Prerequisite: CS102
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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Prerequisite:
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.
Corequisite:
Prerequisite: CS101 CS104
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.
Corequisite:
Prerequisite: CS101 CS111
CS 108 Statistics (Credits: 3)
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.
Corequisite:
Prerequisite: CS107
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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Prerequisite:
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. Instructor-led class time including problem sets and discussions.
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Prerequisite:
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.
Corequisite:
Prerequisite: CS101 CS104
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.
Corequisite:
Prerequisite: CS110
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.
Corequisite:
Prerequisite: CS120 CS111
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. Instructor-led class time including problem sets and discussions.
Corequisite:
Prerequisite: CS120 for CS students, ENGS 115 for ENGS
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.
Corequisite:
Prerequisite: CS121
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.
Corequisite:
Prerequisite: CS101
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.
Corequisite:
Prerequisite: CS105
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.
Corequisite:
Prerequisite: CS121
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.
Corequisite:
Prerequisite: CS102 CS112
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.
Corequisite:
Prerequisite: CS211
CS 217 Computer Graphics (Credits: 3)
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.
Corequisite:
Prerequisite: CS102 CS121
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.
Corequisite:
Prerequisite: CS211
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.
Corequisite:
Prerequisite: CS211
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.
Corequisite:
Prerequisite: CS211
CS 226 Math Modeling Applications (Credits: 3)
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.
Corequisite:
Prerequisite: CS205
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.
Corequisite:
Prerequisite: CS211
CS 246 Artificial Intelligence: Decision Support (Credits: 3)
The course provides an introduction to decision support techniques in the context of artificial intelligence. The main areas to be covered are knowledge representation, planning and reasoning under uncertainty. We will discuss the principles of intellige
Corequisite:
Prerequisite: IESM106
CS 251 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. Instructor-led discussions and problem sets.
Corequisite:
Prerequisite: IESM106
CS 252 Data Science with R (Credits: 3)
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.
Corequisite:
Prerequisite: CS121
CS 296 Capstone (Credits: 3)
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.
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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.
Corequisite:
Prerequisite: CS121
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.
Corequisite:
Prerequisite: CS121
CS 313 Advanced Topics in Algorithms (Credits: 3)
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.
Corequisite:
Prerequisite: CS311
CS 314 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. Instructor-led discussions and problem sets.
Corequisite:
Prerequisite: CS121
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 316 Advanced Cryptography (Credits: 3)
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.
Corequisite:
Prerequisite: CS315
CS 317 Computer Graphics (Credits: 3)
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.
Corequisite:
Prerequisite: CS121
CS 318 Advanced Topics in the Theory of Computation (Credits: 3)
Course Description tailored to course content when offered.
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CS 319 Computer Vision (Credits: 3)
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.
Corequisite:
Prerequisite: CS108
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.
Corequisite:
Prerequisite: CS121
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..
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Prerequisite: CS121
CS 325 Development of Geo-Collaborative Applications (Credits: 2)
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.
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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.
Corequisite:
Prerequisite: CS311
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.
Corequisite:
Prerequisite: CS311
CS 331 Operating Systems (Credits: 3)
The organization and structure of modern operating systems. System level programming in Windows and Unix Operating Systems.
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Prerequisite: CS330 CS320
CS 332 System Administration (Credits: 3)
User administration. Operating system installation, tuning and control. Network administration. Security management. Performance tuning and management.
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Prerequisite: CS331
CS 333 Network Programming (Credits: 3)
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.
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Prerequisite: CS121
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.
Corequisite:
Prerequisite: CS311
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 342 Data Science with R (Credits: 3)
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.
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CS 343 Data Visualisation (Credits: 3)
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.
Corequisite:
Prerequisite: CS108
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
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CS 346 Artificial Intelligence and Decision Support (Credits: 3)
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.
Corequisite:
Prerequisite: CS107
CS 347 Knowledge Representation (Credits: 3)
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.
Corequisite:
Prerequisite:
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.
Corequisite:
Prerequisite:
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.
Corequisite:
Prerequisite:
CS 360 Computational Methods (Credits: 3)
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.
Corequisite:
Prerequisite:
CS 361 Advanced Statistical Modeling (Credits: 3)
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.
Corequisite:
Prerequisite: CS108
CS 362 Time Series Analysis (Credits: 3)
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.
Corequisite:
Prerequisite: CS108
CS 363 Stochastic Models (Credits: 3)
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.
Corequisite:
Prerequisite: CS108
CS 390 Capstone Practicum (Credits: 3)
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.
Corequisite:
Prerequisite:
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.
Corequisite:
Prerequisite:
CS 395 Capstone Preparation (Credits: 3)
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.
Corequisite:
Prerequisite:
CS 396 Capstone-Thesis Writing (Credits: 3)
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.
Corequisite:
Prerequisite: CS395
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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Prerequisite:
CSE 112 Mathematical Thinking (Credits: 3)
Students will explore and develop quantitative analysis and numeracy skills, rooted in logic-based intuition, that are essential to succeed regardless of profession. In this course, students will expand critical thinking skills in the context of understanding and analyzing data and presenting findings/conclusions through the practical application of mathematical theories, principles and techniques rooted in algebra, calculus, probability and statistics in subjects such as demographics, finance, medicine, politics and economics. Through the use of advanced Microsoft Excel functions and formulas, students will expand problem-solving skills. Students will prepare oral and written reports that utilize concepts of the effective visual display of quantitative information to optimize how to summarize and explain mathematical solutions that emphasize clear and effective communication. Instructor-led discussion, along with reading, written, and practical assignments.
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Prerequisite:
CSE 120 Introduction to the World of Programming (Credits: 3)
This course covers the topics related to the role of computers in our everyday life. Topics include high level overview of: history of computers, the architecture of personal computers, mobile devices and other smart gadgets, the structure of internet and cloud, search engines, data storages, data analytics tools, information management tools and information security. Students will learn to write basic programs, implement basic algorithms, collect and store data, browse the data in the web with smart search engines and which is very important understand the key areas of information security. This course is designed for students with no prior background of computer science. Instructor-led discussion, along with reading, and practical assignments.
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Prerequisite:
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.
Corequisite:
Prerequisite: CS100 OR BUS110 or CHSS183
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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Prerequisite:
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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Prerequisite:
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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Prerequisite:
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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Prerequisite:
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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Prerequisite:
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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Prerequisite:
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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Prerequisite:
CSE 190 Engineering for non-Engineers (Credits: 3)
This course aims to give students an insight about basic principles of engineering and its different sub-disciplines. The course will explore the role engineering has played in shaping society today through its various advancements in different fields, e.g. manufacturing, the energy sector, urban development and materials engineering. Student evaluation will be based on individual or group projects, research essays and written examinations. Instructor-led class time. Not available to ENGS students.
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Prerequisite:
CSE 210 Historical Development of Mathematical Ideas (Credits: 3)
This course will provide an exploration into the history, birth and development of mathematical ideas, problems and people behind them. A variety of topics will be covered, such as: infinity and paradoxes; numbers and set theory; algebraic equations and algebra; limits and calculus; shapes, symmetry and geometry; gambling, uncertainty and probability; physics and differential equations; choice and game theory; data analysis and statistics. Students are required to complete problem sets and quizzes, and to complete a group project, as well to conduct collaborative research. Instructor-led discussion, along with reading, written, and practical assignments.
Corequisite:
Prerequisite: One lower division course from those that cluster
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.
Corequisite:
Prerequisite: One lower division course from those that cluster
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.
Corequisite:
Prerequisite: One lower division course from those that cluster
CSE 263 Human Physiology (Credits: 3)
This course aims to build knowledge regarding the interrelationship between the nine organ systems responsible for the healthy functioning of the human body. Analysis will encompass from cells and tissues to the entire organism, underpinning the role of major structures supporting physiological processes. Important diseases will be discussed, including their causes and consequences as examples of disturbed homeostasis and dysfunction of human body systems. Instructor-led discussion, along with reading, written, and practical assignments.
Corequisite:
Prerequisite: One lower division course from those that cluster
CSE 264 The Human Brain (Credits: 3)
The course will cover an introduction to the brain anatomy and the cellular function of neurons, synapses and neurotransmitters. The work of human brain in health and in some disorders as well as the mechanisms of vision, learning, memory, feelings and emotions will be discussed. Applications of the knowledge may be relevant in a variety of realms including for marketing specialists, user interface and software developers as well as public policy makers and educators. Instructor-led discussion, along with reading, written, and practical assignments.
Corequisite:
Prerequisite: One lower division course from those that cluster
CSE 270 Sports Analytics (Credits: 3)
Professional sport organizations are using analytics to make better decisions on team formation, playing strategy etc. Enthusiasts use analytics to predict the outcome of a sporting event and to try to quantify reasons that lead to victory. The course will examine how different statistical and data analytics methods can be used to analyze game-day (in-play) sports data and for pre and post-game sports performance modelling. We will focus on several team games, e.g. soccer, basketball, American football and baseball. The course will use a statistical programming language such as R and assessment may include problem sets, individual or group projects and written examinations. Instructor led class time.
Corequisite:
Prerequisite: One lower division course from those that cluster
CSE 271 Number Statistics and the Environment (Credits: 3)
The course is a practical introduction to general quantitative and statistical techniques that can be applied to geography and environmental studies. Students will learn techniques to verify quality of data, analyzing trends and tendencies, and estimating probability of outcomes. The course will also cover topics such as proposing and verifying hypotheses using numbers and statistical analysis. Each topic will begin with an introduction to a numerical or statistical concept followed by the application of that concept on a real world environmental problem. As the course progresses, students will also be introduced to software that utilizes these concepts. Problem sets and written examinations. Instructor led class time.
Corequisite:
Prerequisite: One lower division course from those that cluster
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.
Corequisite:
Prerequisite: One lower division course from those that cluster
CSE 290 Start-Up Culture (Credits: 3)
Corequisite:
Prerequisite: One lower division course from those that cluster
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.
Corequisite:
Prerequisite: CS100 OR BUS109
ENGS 101 Calculus: Single Variable (Credits: 4)
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.
Corequisite:
Prerequisite:
ENGS 102 Calculus: Multi Variable (Credits: 4)
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.
Corequisite:
Prerequisite: ENGS101
ENGS 103 Linear Algebra and Ordinary Differential Equations (Credits: 4)
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.
Corequisite:
Prerequisite: ENGS102
ENGS 104 Probability and Statistics (Credits: 3)
The topics covered in this introductory course include: axioms of probability; conditional probability, independence; combinatorial analysis; random variables and distributions; expectation, variance, covariance; transformation of random variables; limit theorems, the law of large numbers, the central limit theorem; Markov chains; applications; statistical estimation; correlation, regression; hypothesis testing, maximum likelihood estimation, Bayesian updating; applications. Instructor-led class time including problem sets and discussions.
Corequisite:
Prerequisite: ENGS103
ENGS 110 Introduction to Programming (Credits: 4)
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.
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Prerequisite:
ENGS 115 Data Structures and Algorithms (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. Instructor-led class time including problem sets and discussions.
Corequisite:
Prerequisite: ENGS110 CS111
ENGS 121 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. Instructor-led class time including discussions and problem sets.
Corequisite:
Prerequisite: ENGS101
ENGS 122 Mechanics Lab (Credits: 1)
Hands-on laboratory course to accompany Mechanics. Students will conduct experiments in support of the topics covered in Mechanics.
Corequisite: ENGS121
Prerequisite:
ENGS 123 Electricity and Magnetism (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS101
ENGS 124 Electricity and Magnetism Lab (Credits: 1)
Hands-on laboratory course to accompany Electricity and Magnetism. Students will conduct experiments in support of the topics covered in Electricity and Magnetism.
Corequisite: ENGS 123
Prerequisite:
ENGS 131 Chemistry (Credits: 3)
This course introduces students to principles of chemistry. Topics include atomic theory, periodic properties, stoichiometry, nomenclature, bonding, physical properties of states of matter, solutions, kinetics, equilibrium, acid-base reactions, metathesis reactions, redox reactions, thermodynamics, electrochemistry, and chemical properties of selected classes of compounds. Instructor-led class time including discussions and problem sets.
Corequisite:
Prerequisite:
ENGS 132 Chemistry Lab (Credits: 1)
Hands-on laboratory course to accompany Chemistry. Students will conduct experiments in support of the topics covered in Chemistry.
Corequisite: ENGS131
Prerequisite:
ENGS 135 Introduction to Chemical Engineering (Credits: 3)
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Prerequisite:
ENGS 141 Engineering Statics (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS121
ENGS 142 Engineering Dynamics (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS141
ENGS 151 Circuits (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS123
ENGS 152 Circuits Lab (Credits: 1)
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.0
Corequisite: ENGS152
Prerequisite:
ENGS 171 Biology (Credits: 3)
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ENGS 174 Biotechnology (Credits: 3)
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ENGS 176 Environmental Engineering (Credits: 3)
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ENGS 181 Introduction to Materials Science (Credits: 3)
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ENGS 211 Numerical Methods (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS103 ENGS115
ENGS 241 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. Instructor-led class time including problem sets and discussions.
Corequisite:
Prerequisite: ENGS141
ENGS 245 Thermodynamics (Credits: 3)
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ENGS 246 Heat Transfer (Credits: 3)
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ENGS 248 Introduction to Fluid Mechanics (Credits: 3)
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ENGS 251 Embedded Systems (Credits: 3)
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 as well as experimentation using hardware/software equipment. Instructor-led class time including problem sets and discussions.0
Corequisite:
Prerequisite: CS130 ENGS151
ENGS 252 Signals and Systems (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS142 ENGS151
ENGS 261 Control Systems 1 (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS252
ENGS 262 Control Systems 1 Lab (Credits: 1)
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.
Corequisite: ENGS261
Prerequisite:
ENGS 263 Control Systems 2 (Credits: 3)
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.
Corequisite:
Prerequisite: ENGS261
ENGS 264 Control Systems 2 Lab (Credits: 1)
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ENGS 265 Mechatronics Design (Credits: 3)
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ENGS 275 Resource Management (Credits: 3)
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ENGS 276 Project Management (Credits: 3)
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ENGS 280 Alternative Energy (Credits: 3)
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ENGS 299 Capstone (Credits: 3)
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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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Prerequisite:
IESM 106 Probability and Statistics (Credits: 3)
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.
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Prerequisite:
IESM 220 Operations Research 1 (Credits: 3)
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.
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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 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.
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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.
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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.
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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.
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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.
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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 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 Managing Engineering and Technology (Credits: 3)
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.
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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 Sustainable Smart and Resource Efficient Systems 1: Systems and Technologies (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, 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.
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IESM 352 Sustainable Smart and Resource Efficient Systems 2: Decision Making Tools (Credits: 3)
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.
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Prerequisite: IESM360 IESM361
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.
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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: 2)
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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Prerequisite: IESM395
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.
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Prerequisite: IESM395