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Course Criteria
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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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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. 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 6110 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
Students learn the significance and process of including end users in the technology design loop. Students learn principles of human-computer interaction along with methods of conducting human-subject studies to identify user needs and experiences with technology use.
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1.00 Credits
This course covers essential cloud-computing concepts, including computer, storage, database, networking, and security services. Students gain an overall understanding of these concepts, along with some initial skills for working with the services of a specific cloud provider. 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 Corequisite: CS 5260 Repeatable for credit: No Grade Mode: Standard
Corequisite:
CS 5260
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2.00 Credits
This course addresses developing and deploying of secure, available, scalable, reliable, and durable software applications in a cloud environment. Students gain hands-on skills with at least one specific cloud environment. Corequisite: CS 5250
Corequisite:
CS 5250
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4.00 Credits
Review of programming language structures, translation, loading, execution, and storage allocation. Compilation of declarations, expressions, statements, and procedures/functions. Organization and design of a compiler. Prerequisite(s): CS 2810 or instructor permission; 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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4.00 Credits
Introduction to concepts of graphical techniques. Digital and pictorial representation of information. Prerequisite(s): GPA of 2.5 or higher; CS 2420 with a grade of C- or better; MATH 2250 or MATH 2270 with a grade of C- or better Registration Restriction(s): Not available to pre-Computer Science majors
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4.00 Credits
In this course, students explore technical game development. The course emphasizes integration of multiple computer science topics within a single application, including: graphics, AI, multi-threading, multi-core, networking, synchronization, optimization, and scripting languages. This is a TEAMWORK course. Prerequisite(s): CS 2420, CS 3100; 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 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): GPA of 2.5 or higher; CS 2420 with a C- or better; MATH 2250 or MATH 2270 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 Dual-listed as: CS 6470
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4.00 Credits
Students explore the field of robotics through the lens of decision-making algorithms. They examine critical aspects of autonomous systems from a machine learning and data science perspective, with emphasis on sensing, high-level objective planning, motion planning, and human interaction. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. 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 or graduate students in the Computer Science department Repeatable for credit: No Grade Mode: Standard Dual-listed as: CS 6510
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