Course Search Results

• CS 5470: Introduction to Interactive Virtual Reality

  3.00 Credits

  Utah State University

  
  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.
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• CS 5510: Robot Intelligence

  4.00 Credits

  Utah State University

  
  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.
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• CS 5600: Intelligent Systems

  4.00 Credits

  Utah State University

  
  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.
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• CS 5615: Natural Language Processing

  3.00 Credits

  Utah State University

  
  This course offers students an introduction to the field of NLP, providing a comprehensive overview of state-of-the-art methods within this domain. Integrating hands-on sessions, the course aims to equip students with fundamental knowledge of AI and natural language. Additional coursework is required for those enrolled in the graduate-level course.
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• CS 5620: AI Applications In Education

  3.00 Credits

  Utah State University

  
  This course introduces students to cognitive modeling, learner modeling, intelligent tutoring systems, adaptive educational systems, and natural language processing for automated feedback. Coursework involves using AI tools for prototype development and a course project of the student's own design. Additional coursework is required for those enrolled in the graduate-level course..
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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??????.
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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 at scale (e.g., big data). Specifically, students study big data management and processing techniques, data analytics, statistical methods & models, and data visualization. Additional coursework is required for those enrolled in the graduate-level course??????.
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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.
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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.
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• CS 5710: Software Security

  3.00 Credits

  Utah State University

  
  Students learn security concepts, methodologies, and techniques for operating systems and application software, including techniques like source code analysis, binary analysis, fuzzing, and reverse engineering, with applications in vulnerability discovery, malware analysis, risk mitigation, and digital forensics. Additional coursework is required for those enrolled in the graduate-level course.
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