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Course Criteria
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1.00 Credits
Covers current research issues involving the implementation, evaluation and design of user interfaces, while also providing a basic background in the fundamentals of user interface evaluation, programming, tools, and techniques. A possible topic is programming and designing device-independent interfaces. Previous topics have included the development of pervasive internet-based interfaces and software visualization.Prerequisite: Consent of instructor.
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1.00 Credits
Programming tools; control and data integration; software understanding and debugging; environments for parallel and distributed programming; reverse engineering; configuration management and version control and debugging. Emphasis on current research areas. Prerequisite: consent of instructor.
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1.00 Credits
Topics in the design, specification, construction and validation of programs. Focus will be on tools to support each of these stages. Course will pay special attention to the concerns raised by the properties of modern software systems including distribution, security, component-based decomposition and implicit control. Recommended: CSCI 1900 or other upper-level systems coursework.
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1.00 Credits
The solution of scientific problems using computer graphics and visualization. Working in small multidisciplinary groups, students identify scientific problems, propose solutions involving computational modeling and visualization, design and implement the solutions, apply them to the problems, and evaluate their success. Examples include interactive software systems, immersive CAVE applications, or new applications of existing visualization methods. Prerequisites: all: programming experience; CS students: graphics experience; others: problem ideas. Instructor permission required.
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1.00 Credits
Various topics in computer understanding of natural language, primarily from a statistical point of view. Topics include: hidden Markov models, word-tagging models, probabilistic context-free grammars, syntactic disambiguation, semantic word clustering, word-sense disambiguation, machine translation and lexical semantics. Prerequisite: CS 141 (CSCI 1410).
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1.00 Credits
Machine learning from the artificial intelligence perspective, with emphasis on empirical validation of learning algorithms. Different learning problems are considered, including concept learning, clustering, speed-up learning, and behavior learning. For each problem a variety of solutions are investigated, including those from symbolic AI, neural and genetic algorithms, and standard statistical methods. Prerequisite: CS 141 or familiarity with basic logic and probability theory.
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1.00 Credits
This course surveys recent developments in an emerging area known as game-theoretic artificial intelligence (AI), which incorporates fundamental principles of game theory into AI. Research in this area is motivated by game-theoretic applications, such as auction design and voting, as well as AI application areas, such as multiagent systems. Students will conduct theoretical, empirical, and experimental investigations, asking fundamental questions such as: can the behavior of computational learning agents converge to game-theoretic equilibria? Prerequisite: Consent of instructor.
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1.00 Credits
Typically, an algorithm solves one problem, whereas a well-designed data structure can help implement algorithms for a wide variety of problems. We will study the design, analysis and implementation of advanced data structures. Focus is on data structures that are fast, both theoretically and empirically. Prerequisite: CSCI 1570 or the equivalent.
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1.00 Credits
Typically, an algorithm solves one problem, whereas a well-designed data structure can help implement algorithms for a wide variety of problems. We will study the design, analysis and implementation of advanced data structures. Focus is on data structures that are fast, both theoretically and empirically. Prerequisite: CSCI 1570 or the equivalent.
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1.00 Credits
Planar graphs arise in applications such as road map navigation and logistics, graph drawing and image processing. We will study graph algorithms and data structures that exploit planarity. Our focus will be on recent research results in optimization. Prerequisite: CSCI 1570 or the equivalent.
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