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Course Criteria
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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(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 6600
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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. 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 6620 Repeatable for credit: No Grade Mode: Standard
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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. 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 6640
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3.00 Credits
This course introduces the theoretical foundations, algorithms, and methods of deriving valuable insights from data. Students learn how to manage and analyze data at scale (e.g., big data). Specifically, students study big data management and processing techniques, data analytics, statistical methods and models, data visualization, and etc. Project required. 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 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course provides an introduction to theories and techniques of machine intelligence, with emphasis on image processing, pattern recognition, and computer vision. 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 2270 with a C- or better; STAT 2300 or STAT 3000 or MATH 5710 with a C- or better Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Dual-listed as: CS 6680 Repeatable for credit: No Grade Mode: Standard
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4.00 Credits
Students learn concepts of object-oriented software development (OOSD) and how to use those concepts during design, implementation, and testing to improve software quality. The course focuses on core OO principles, design solutions to reoccurring problems, best practices, and pitfalls. Prerequisite(s): CS 3450; 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 introduces topics in computing education research, including learning science, cognitive science, motivation and affect, statistical methods, qualitative methods, pedagogy, assessment, equity and diversity, programming paradigms, and computing for other disciplines. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): GPA of 2.5 or higher; STAT 2300 or STAT 3000 Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Dual-listed as: CS 6750 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course compares of various database systems, normal forms, protection, concurrency, security and integrity, and distributed and object-oriented systems. 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 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course introduces design principles and techniques for visualizing data. Students learn how visual representations support analysis and understanding of complex data and how to design effective and interactive visualizations. 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 Dual-listed as: CS 6820 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
In this project-based course, students from multiple disciplines work in teams to analyze data from real-world projects. Project teams group technical students from CS with domain experts from outside of CS to analyze data using state-of-the-art data science tools and techniques. 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 Repeatable for credit: No Grade Mode: Standard
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