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Course Criteria
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3.00 Credits
This course surveys technologies represented in networked software systems based on the client-server model, including graphics user interfaces (GUIs) and databases, and features a team project to design and build such a system with interdisciplinary application. Topics include design of client-server systems, GUI applications, event-driven programming, APIs, databases and SQL query language, embedded queries, data modeling, concurrency, network programming, security and ethical considerations, and team programming methodologies. Prerequisites: Computer Science 225 or 251, or permission of the instructor.
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3.00 Credits
This is an intermediate-level version of Computer Science 394.
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3.00 Credits
Recent and planned topics include distributed computing and parallel algorithms, computer graphics, relational database systems, and real-time systems. May be repeated if topics are different.
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3.00 Credits
This course addresses computational problems arising from the need to store, access, transform, and utilize DNA-related data. Topics from computer science central to the development of problem-solving tools include: exhaustive search; algorithms (including dynamic programming, divide-and-conquer, graph and greedy algorithms); combinatorial pattern matching; clustering and trees; and hidden Markov models. Prerequisites: Computer Science 253, or Computer Science 251 and Biology 225, or equivalent computer science and biology study, or permission of instructor.
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3.00 Credits
Students learn about formal languages, automata, and other topics concerned with the theoretical basis and limitations of computation. The course covers automata theory including regular languages and context-free languages, computability theory, complexity theory including classes P and NP, and cryptographic algorithms. Prerequisite: Computer Science 231 or permission of instructor.
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3.00 Credits
Students learn a style of programming based on first order predicate logic. Topics include declarative programming, Horn clauses, declarative and procedural semantics of logic programs, relations clauses, goals, backtracking, and resolution. Programming projects and exercise use Prolog, the most significant logic programming language. Additional topics include the relationship of Prolog to logic and applications to artificial intelligence. Prerequisites: Computer Science 253 or 276 or permission of instructor.
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3.00 Credits
Class members participate in undergraduate research, including readings from the research literature, team development of project software, ethical analysis of the project applying Computer Science 263 principles, and writing a research paper for public presentation. Projects are frequently interdisciplinary in nature, and build on prior undergraduate research experiences among class members. Prerequisites: major in computer science with senior standing, and completion of or concurrent enrollment in computer science core courses, including Computer Science 263, or permission of instructor.
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3.00 Credits
Students gain experience in computer-industry positions. Projects have included implementation of solid modeling (NURBS), documentation, business applications of computing, applications of computer graphics in medical research, team programming. Internship experiences (whether for credit or not) are strongly encouraged for anyone considering a career in computing.
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3.00 Credits
This course provides a comprehensive research opportunity, including an introduction to relevant background material, technical instruction, identification of a meaningful project, and data collection. The topic is determined by the faculty member in charge of the course and may relate to his/her research interests. Prerequisite: Determined by individual instructor. Offer based on department decision.
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3.00 Credits
Recent projects, usually executed by individuals and coordinated with ongoing undergraduate research projects, include extension of a networked academic planning system, preliminary preparations for a system for managing web-based portfolios, neural networks, random number generation, applications of genetic algorithms to scheduling, computational Bayesian image reconstruction, design and implementation of an interface language for linear programming software, and design of software to assist choreographers.
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