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Course Criteria
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3.00 Credits
Students learn to deliver scalable, high-quality, and responsive web applications using the latest technology. Topics include single-page applications, asset management, real-time communication, analytics, responsive user-experience design and techniques, accessibility, high-coverage unit testing, and publishing/hosting with cloud providers. Prerequisites/Restrictions: CS 2610
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3.00 Credits
Theories of programming design and implementation. Introduction to variety of programming languages, showing how they represent trade-offs with respect to these theories. Prerequisite/Restriction: 2.0 GPA; grade of C- or better in CS 2420. Not available to pre-Computer Science majors.
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3.00 Credits
Current topics in computer science as determined by advances in the field. Prerequisite/Restriction: 2.0 GPA; grade of C- or better in CS 2420. Not available to pre-Computer Science majors.
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1.00 - 4.00 Credits
Participation in research projects, under supervision of a computer science faculty member. Prerequisite/Restriction: 2.0 GPA; grade of C- or better in CS 2420 and permission of instructor. Not available to pre-Computer Science majors. Repeatable for credit.
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3.00 Credits
This course explores the theory of computation, including presentation of computability, decidability, and complexity. This course includes formal grammars, finite and pushdown automata, and turing machines. Prerequisite(s): GPA 2.5 or higher; CS 2420 with a C- or better Registration Restriction(s): Not available to pre-Computer Science majors; Admission to Computer Science - BS. Repeatable for credit: No Grade Mode: Standard
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4.00 Credits
This course introduces high-performance computing, leveraging parallel computing power to solve complex computational problems faster using clusters and supercomputers. Students learn the main programming models, optimized coding for modern multi-core processors, clusters, and modern computing architecture. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): Minimum GPA of 2.5; CS 2420 with a C- or better Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course introduces principles, methods and techniques for visual analysis of scientific data. Students create visualization of scalar, vector and tensor field data using state-of-the-art techniques. They acquire hands-on experience using visualization software on real science and engineering use cases. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): CS 2420 with a grade of C- or better; a minimum GPA of 2.5 Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Dual-listed as: CS 5040 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
Study of algorithms and their analysis, including: design by induction, algorithms involving sequences and sets, graph algorithms, geometric algorithms, algebraic algorithms, reductions, NP-completeness, and parallel algorithms. Prerequisite(s): CS 2420 with a grade of C- or better; a minimum GPA of 2.5 Registration Restriction(s): Not available to pre-Computer Science majors Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
Students develop end-to-end tools for decision making under uncertainty. Key concepts include detecting uncertainty, working with little to no data, rapid adaption, and decision-making algorithms in multiple fields (robotics, fintech, economics, sociology). Course material is applied in real-world projects. Prerequisite(s): CS 2420 with a grade of C- or better; a minimum GPA of 2.5 Registration Restriction(s): Not available to pre-Computer Science majors Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course examines state-of-the-art research on time series (TS) data mining. It includes the applications of TS data in finance, medicine and numerous fields in science and engineering. Topics covered include data representation, anomaly detection, similarity search, classification, visualization of TS, etc. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): GPA of 2.5 or higher; CS 2420 with a B or better Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Repeatable for credit: No Grade Mode: Standard
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