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Course Criteria
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3.00 Credits
This course consists of in-depth involvement in ongoing programming projects under direct professional supervision. This course may not be used as an elective in either the Computer Science major or the Computer Security major.
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3.00 Credits
This course consists of involvement in ongoing network security tactics, techniques and procedures under direct professional supervision. This course may not be used as an elective in either the Computer Security major or the Computer Science major.
Prerequisite:
CPSC130 AND CPSC131 AND CPSC141 AND CPSC230 AND CPSC250 AND CPSC270 AND CPSC445
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3.00 Credits
This course consists of involvement in ongoing network security tactics, techniques and procedures under direct professional supervision. This course may not be used as an elective in either the Computer Security major or the Computer Science major.
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3.00 Credits
This course is an introduction to computer graphics. Basic principles for design, use, and understanding of graphics systems will be studied. Algorithms for creating and manipulating graphic displays and a standard programming language for their implementation will be presented. There will be programming practice. Prerequisite: Ability to program in "C" or "C++".
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3.00 Credits
This course introduces students to dynamic, linear, and integer programming algorithms. There will be programming practice involving these algorithms.
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3.00 Credits
Search engine systems like Google and Yahoo! are essential tools for finding the most valuable and relevant information, given the explosion of digital information available today. This course studies the design, implementation, and evaluation of modern information retrieval systems, such as web search engines. Topics include: retrieval system design and implementation, text analysis techniques, retrieval models (e.g. Boolean, vector space, probabilistic, and learning-based methods), search evaluation, retrieval feedback, search log mining, and applications in web information management. The course also covers advanced topics in information retrieval, including document classification and clustering, recommendation systems, collaborative filtering, and personalized retrieval.
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3.00 Credits
This course is an introduction to knowledge-based systems. Basic concepts, characteristics, architectures, and tools will be studied. Major paradigms for synthesis and analysis class systems, and exact and inexact reasoning systems will be discussed. Computational and knowledge engineering issues will be treated by case studies and there will be programming practice.
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3.00 Credits
This course is an introduction to robotics on a technical level. The history of robotics, computer-aided manufacturing, robot components, sensors, programming systems, applications, and future implications of robotics technology will be studied. There will be hands-on experience with a robot.
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3.00 Credits
This course is an introduction to artificial intelligence and heuristic programming techniques. Search strategies, games, heuristic mechanisms, and automated deduction will be studied. There will be programming practice. For graduate credit, a student will be required to write a term paper or execute a project which reflects deeper investigation of the topics covered in the course.
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3.00 Credits
This course provides students with a broad introduction to machine learning, datamining, and statistical patter recognition. Students will study data exploration, decision-tree, K-nearest, neighborhoods, linear regression, logistic regression, support vector machines, neural networks, ensemble learning, clustering, dimensionality reduction evaluations. Students will be required to build predictive models based on machine algorithms. For graduate credit a student will be required to write a term paper or execute a project which reflects deeper investigation of the topics covered in the course.
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