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Course Criteria
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1.00 - 4.00 Credits
Current topics in computer science as determined by advances in the field. 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 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course provides the independent study of selected topics. Prerequisite(s): GPA of 2.5 or higher; CS 2420 with a C- or better Registration Restriction(s): Not available to pre-Computer Science majors Registration Restriction Special Approval: Instructor permission 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. Cross/Dual Listed as: CS 5030 Prerequisites/Restrictions: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
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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. Crosslisted as: CS 5040 Prerequisites/Restrictions: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
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3.00 Credits
This course presents algorithms and data structures of computational geometry. Students learn algorithm design techniques for solving geometric problems as well as their applications in data processing, computer graphics, robotics, computer-aided design, and many others. CS 6050 and CS 7050 are cross listed, but CS 7050 requires additional work. Prerequisite: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science Also Taught as: CS 7050
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4.00 Credits
This course introduces software systems that are both autonomous and social, engaging in cooperation, coordination, and negotiation. Students study coalitions, auctions, game theory, voting systems, and types of agents. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. Registration Restriction(s): Enrollment in one of the following programs, or instructor permission: Master of Computer Science - MCS Computer Science - MS Data Science - MS Computer Science - PhD Dual-listed as: CS 5110 Repeatable for credit: No Grade Mode: Standard
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1.00 - 9.00 Credits
Provides credit for students working at a participating firm under faculty supervision. Prerequisite/Restriction: 3.0 GPA; permission of instructor and enrollment in Computer Science master's or PhD program. Repeatable for credit. Pass/Fail only.
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3.00 Credits
Study of computer rendering of three-dimensional objects. Object representation, hidden surface removal, and shading. Ray tracing of synthetic scenes using mathematically defined surfaces. Prerequisites: 3.0 GPA; grade of B- or better in CS 5400 and enrollment in Computer Science master's or PhD program.
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3.00 Credits
This course addresses human factors of privacy and security, with emphasis on user's privacy perceptions, security behavior, and designing and building secure systems with a human-centric focus. Students apply basic principles of human-computer interaction in designing secure and privacy-protective systems. Prerequisite: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
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3.00 Credits
Students learn the core and state-of-art technologies of Virtual Reality (VR). Topics include head-tracked and head-mounted displays, 3D tracking, 3D user interfaces and interactions, VR applications, human perception, evaluation of VR, and other VR-related topics. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): CS 2420; MATH 2250 or MATH 2270 Registration Restriction(s): Enrollment in one of the following programs, or instructor permission: Computer Science - Master of Computer Science - MCS, Computer Science - MS, Data Science - MS, Computer Science - PhD Repeatable for credit: N Grade Mode: Standard
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