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Course Criteria
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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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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. Cross/Dual Listed as: CS 5510 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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4.00 Credits
This course examines the three main areas of AI: data-driven intelligence, natural language processing (NLP), and planning. Students learn models from big data, investigate systems that understand/generate natural language, and study problem-solving models in domains such as robot navigation and symbolic mathematics. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite/Restriction: 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 Cross-listed as: CS 5600.
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3.00 Credits
This course examines the need to transition the world's economy to low-carbon sources and explores how artificial intelligence is enabling and optimizing this transition. Each student completes a project applying artificial intelligence methods to a specific clean energy problem. 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 5620 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course introduces students to machine learning and problem solving techniques based on fuzzy logic. Prerequisites: 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 explores the influence of reinforcement learning in constructing software systems that learn from external environment interaction. The course reviews reinforcement learning foundations and advanced techniques. Students work on a practical project in applications or advancing methodology. Additional coursework is required for those enrolled in the graduate-level course. Crosslisted as: CS 5640 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 program
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3.00 Credits
This advanced course covers theories and techniques of machine intelligence using neural networks. It emphasizes various neural network paradigms and the types of problems they are best suited to solve. Prerequisite/Restriction: 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 provides a practical and mathematical introduction to machine learning techniques and principles in supervised and unsupervised settings. Students learn to understand machine learning research papers and gain the practical experience to implement machine learning approaches on real data. Prerequisites/Restrictions: Graduate standing or: MATH 1220 STAT 3000 or MATH 5710 (MATH 5710 preferred) MATH 2210 or MATH/STAT 5645/6645 (MATH/STAT 5645/6645 preferred) MATH 2270 or MATH/STAT 5645/6645 (MATH/STAT 5645/6645 preferred) Experience programming in Python, R, or Matlab is essential for success in the course Cross listed as: STAT 6655
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