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  • CS 5640: Artificial Neural Networks

    3.00 Credits
    Utah State University

    This course is designed for students to acquire foundational knowledge about deep neural networks and their applications across various AI tasks, including language understanding, speech and image recognition, machine translation, planning, as well as other 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 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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  • CS 5665: Machine Learning for Data Science

    3.00 Credits
    Utah State University

    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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  • CS 5680: Computer Vision: Foundations and Applications

    3.00 Credits
    Utah State University

    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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  • CS 5700: Object-Oriented Software Development

    4.00 Credits
    Utah State University

    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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  • CS 5750: Computing Education Research

    3.00 Credits
    Utah State University

    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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  • CS 5800: Introduction to Database Systems

    3.00 Credits
    Utah State University

    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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  • CS 5820: Interactive Information Visualization for Data Science

    3.00 Credits
    Utah State University

    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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  • CS 5830: Data Science in Practice

    3.00 Credits
    Utah State University

    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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  • CS 5840: Graph Mining

    3.00 Credits
    Utah State University

    This course introduces data mining techniques (e.g., graph neural networks) to extract meaningful patterns from graph data. The course also explores social network analysis as the study of social ties through the lens of graph theory. 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 Prerequisite Recommendation(s): MATH 2270; CS 5665 Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Dual-listed as: CS 6840 Repeatable for credit: No Grade Mode: Standard
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  • CS 5850: Introduction to Data Analysis

    3.00 Credits
    Utah State University

    This course is intended for students interested in gaining hands-on experience applying computational techniques to solve big data analysis problems. The introduced topics help students learn practical tools to perform the necessary steps of a data analysis project pipeline. 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 6850 Repeatable for credit: No Grade Mode: Standard
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