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Course Criteria
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3.00 Credits
09X: Arrange. This course provides a practical and principled coverage of useful numerical and computational tools of use in many disciplines. The first half of this course provides the mathematical (linear algebra) and computing (Matlab) framework upon which data analysis tools are presented. These tools include data fitting, Fourier analysis, dimensionality reduction, estimation, clustering, and pattern recognition. This course is designed for undergraduate and graduate students across the sciences and social sciences. Prerequisite: Computer Science 5 or equivalent, Mathematics 8 or equivalent, Mathematics 22 or Mathematics 24 or equivalent, as approved by the instructor. Dist: TAS. Farid.
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3.00 Credits
09S: 11 09X: Arrange The architecture and organization of a simple computer system is studied. Topics covered include how information is represented in memory, machine-language instructions and how they can be implemented at the digital logic level and microcode level, assembly language programming, and input/output operations. Speedup techniques, such as pipelining and caching, are also covered. Prerequisite: Computer Science 5 or Engineering Sciences 20. Dist: TAS. Smith (spring).
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3.00 Credits
09W, 09F: Arrange The migration of important social processes to distributed, electronic systems raises critical security and privacy issues. Precisely defining security and privacy is difficult; designing and deploying systems that provide these properties is even harder. This course examines what security and privacy mean in these settings, the techniques that might help, and how to use these techniques effectively. Our intention is to equip computer professionals with the breadth of knowledge necessary to navigate this emerging area. Prerequisite: completion of Computer Science 23 and completion of (or concurrent enrollment in) Computer Science 37; or instructor's permission. Computer Science 19 is recommended. Dist: TAS. Palmer.
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3.00 Credits
09W: 11 10W: Arrange This course serves as an introduction to formal models of languages and computation. Topics covered include finite automata, regular languages, context-free languages, pushdown automata, Turing machines, computability, and NP-completeness. Prerequisite: Computer Science 25 (students who have not taken Computer Science 25, but have a strong mathematical background, may take Computer Science 39 with permission of the instructor). Dist: QDS. Chakrabarti.
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3.00 Credits
09X: Arrange This course provides an overview of computing and computer science, including such topics as the history of computers, computer applications, introductory concepts in digital electronics and computer architecture, computer languages, theory of computation, artificial intelligence, and the impact that computers have had on society and are likely to have in the future. Students will be introduced to computing through appropriate high-level software, such as World Wide Web, hypertext, and scripting languages. For example, in recent offerings students learned HTML and Javascript, and wrote significant HTML projects. This course is intended for students who plan to take only one course in computer science, although students who take this course are welcome to take Computer Science 5 later. This course is not open to students who have passed Computer Science 5 or Engineering Sciences 20 or who have received credit for one of these courses via the Advanced Placement exam or the local placement exam. Dist: TAS. Farid.
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3.00 Credits
09S: 10A 10S: Arrange This is the culminating course for the Digital Arts Minor. Students from arts and sciences come together to complete projects in digital arts, including: 3D computer animations; innovative digital installations; creative mobile media; interactive pieces; 2D digital projects. Students work in small teams to complete work of a high production quality or work that incorporates innovations in technology. This course has a required laboratory period. Prerequisite: Computer Science 32 and one of the following courses: Film Studies 31, 35, 38; Studio Art 16, 29; Theater 30; Computer Science 12, 42, 82; or Psychology 21. Dist: ART. Pellacini.
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3.00 Credits
09S: 2A 10S: Arrange Bioinformatics is broadly defined as the study of molecular biological information, and this course introduces computational techniques for the analysis of biomolecular sequence, structure, and function. While the course is application-driven, it focuses on the underlying algorithms and information processing techniques, employing approaches from search, optimization, pattern recognition, and so forth. The course is hand-on: programming lab assignments provide the opportunity to implement and study key algorithms. Prerequisite: Computer Science 8. Computer Science 19 is recommended. Dist. TLA. Bailey-Kellogg.
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3.00 Credits
09W: 10 10W: Arrange An introduction to the field of Artificial Intelligence. Topics include games, robotics, Lisp, Prolog, image understanding, knowledge representation, logic and theorem proving, understanding of natural languages, and discussions of human intelligence. Prerequisite: Computer Science 8. Computer Science 19 is recommended. Dist: TAS. Zomorodian.
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3.00 Credits
09S: 11 10S: Arrange Planning, scheduling, and design problems in large organizations, economic or engineering systems can often be modeled mathematically using variables satisfying linear equations and inequalities. This course explores these models: the types of problems that can be handled, their formulation, solution, and interpretation. It introduces the theory underlying linear programming, a natural extension of linear algebra that captures these types of models, and also studies the process of modeling concrete problems, the algorithms to solve these models, and the solution and analysis of these problems using a modeling language. It also discusses the relation of linear programming to the more complex frameworks of nonlinear programming and integer programming. These paradigms broaden linear programming to respectively allow for nonlinear equations and inequalities, or for variables to be constrained to be integers. Prerequisites: Computer Science 3, 8, or 19; Mathematics 8; Mathematics 22 or 24; or permission of the instructor. Dist: TAS. Fleischer.
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3.00 Credits
09S: 12 09X: 9 10S: 12 Laboratory This course teaches classical switching theory including Boolean algebra, logic minimization, algorithmic state machine abstractions, and synchronous system design. This theory is then applied to digital electronic design. Techniques of logic implementation, from Small Scale Integration (SSI) through Application-Specific Integrated Circuits (ASICs), are encountered. There are weekly laboratory exercises for the first part of the course followed by a digital design project in which the student designs and builds a large system of his or her choice. In the process, Computer-Aided Design (CAD) and construction techniques for digital systems are learned. Dist: TLA. Cooley (spring), Hansen (summer).
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