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Course Criteria
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3.00 Credits
Every fall. STEPHEN MAJERCIK. The study of algorithms concerns programming for computational efficiency, as well as problem-solving techniques. The course covers practical algorithms and theoretical issues in the design and analysis of algorithms. Topics include divide and conquer algorithms, greedy algorithms, dynamic programming, approximation algorithms, and a study of intractable problems. Prerequisite: Computer Science 210 and either Computer Science 189 or Mathematics 200, or permission of the instructor.
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3.00 Credits
Every spring. ERIC CHOWN. Focuses on different paradigms for solving problems, and their representation in programming languages. These paradigms correspond to distinct ways of thinking about problems. For example, "functional" languages (such as Haskell) focus attention on the behavioralaspects of the real-world phenomena being modeled; "logic programming" languages (suchas Prolog) focus attention on the declarative aspects of problem-solving. Covers principles of language design and implementation including syntax, semantics, type systems, control structures, and compilers. Prerequisite: Computer Science 210 and either Computer Science 189 or Mathematics 200.
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3.00 Credits
Fall 2006. ALLEN TUCKER. Studies the process for designing complex software applications, with a special focus on the use of formal design and verification methods. The study of formal methods includes contemporary methodologies and tools like "designs by contract," the Unified ModelingLanguage (UML), and the Java Modeling Language (JML). Students evaluate the overall strengths and limitations of formal specification and verification in the software design process. A substantial software design project is used as a case study for working with various concepts, tools, and techniques in a laboratory setting. Prerequisite: Computer Science 210 and either Computer Science 189 or Mathematics 200.
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3.00 Credits
Spring 2008. ERIC CHOWN. Explores the principles and techniques involved in programming computers to do tasks that would require intelligence if people did them. State-space and heuristic search techniques, logic and other knowledge representations, reinforcement neural network learning, and other approaches are applied to a variety of problems with an emphasis on agentbased approaches.
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3.00 Credits
Every spring. ADRIANA PALACIO. What is computation This course studies this question, and examines the principles that determine what computational capabilities are required to solve particular classes of problems. Topics include an introduction to the connections between language theory and models of computation, and a study of unsolvable problems. Prerequisite: Computer Science 189 or Mathematics 200, or permission of the instructor.
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3.00 Credits
THE DEPARTMENT.
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3.00 Credits
Fall 2006. ERIC CHOWN. Robotics is a challenging discipline that encourages students to apply theoretical ideas from a number of different areas-artificial intelligence, cognitive science, operations research-in pursuit of an exciting, practical application: programming robots to do useful tasks. Two of the biggest challenges are building effective models of the world using inaccurate and limited sensors, and using such models for efficient robotic planning and control. Addresses these problems from both a theoretical perspective (computational complexity and algorithm development) and a practical perspective (systems and human/ robot interaction) through multiple programming projects involving simulated and actual robots. Prerequisite: Computer Science 210 and either Computer Science 189 or Mathematics 200.
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3.00 Credits
Fall 2006. ADRIANA PALACIO. The smooth functioning of our society increasingly depends on the flow of information through computer networks. Problems of privacy, authenticity, and security of information have become extremely important, and cryptography is an essential tool in addressing these issues. Covers cryptographic techniques and their application to real-world network security problems. Topics include mathematics of cryptography, cryptographic algorithms, computational issues in cryptography, real-world security systems, and social and political issues surrounding cryptography and security. Prerequisite: Computer Science 210 and either Computer Science 189 or Mathematics 200, or permission of the instructor.
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3.00 Credits
MCSR.Spatial Data Structures
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3.00 Credits
MCSR.GIS Algorithms and Data Structures
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