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Course Criteria
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3.00 Credits
PQ: CMSC 15400. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. The focus is on real-time rendering techniques, such as those found in computer games. These include coordinate systems and transformations, the graphics pipeline, basic geometric algorithms, texture mapping, level-of detail optimizations, and shadows. Students are required to complete both written assignments and programming projects using OpenGL. This course is offered in alternate years. J. Reppy. Winter.
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3.00 Credits
PQ: Strong programming skills and basic knowledge of linear algebra and calculus. Scientific visualization combines the image synthesis methods of computer graphics, numerical methods of scientific computing, and mathematical models of the physical world to create a visual framework for discovering, understanding, and solving scientific problems. This course describes the context, methods, and application of modern scientific visualization, giving students the skills required to evaluate, design, and create effective visualizations. This course, which uses mainly Python software and packages that have convenient Python interfaces, is also intended for nonmajors with scientific data visualization needs. This course is offered in alternate years. G. Kindlmann. Spring. Not offered 2009 C10; will be offered 201 0 -11.
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3.00 Credits
PQ: CMSC 15400; 23700 or 25000; and one of 23000, 23300, or 23500. Strong background in programming and expertise in at least two technical areas underlying computer games (e.g., graphics, scientific computing, networking). Computer games are one of the most exciting applications of computer technology. They also are large software systems that embody cutting-edge graphics, as well as techniques from scientific simulation, networking, AI, and databases. This course introduces issues of computer game construction. Students work in teams to design and create games using existing libraries for graphics, physics simulation, and so forth. This course is offered in alternate years. J. Reppy. Spring.
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3.00 Credits
PQ: CMSC 15300. This course introduces the theoretical, technical, and philosophical issues of AI. We emphasize computational and mathematical modes of inquiry into the structure and function of intelligent systems. Topics include learning and inference, speech and language, vision and robotics, and search and reasoning. Winter.
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3.00 Credits
PQ: CMSC 15200 or 12200, or knowledge of C++. This course introduces the problems of computational linguistics and the techniques used to deal with them. Topics are drawn primarily from phonology, morphology, and syntax. Special topics include automatic learning of grammatical structure and the treatment of languages other than English. J. Goldsmith. Autumn.
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3.00 Credits
PQ: CMSC 25010. This course covers computational models of speech, including acoustic-phonetic recognition, speech processing, and Fourier analysis. P. Niyogi. Spring. Not offered 2009 C10; will be offered 201 0 -11.
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3.00 Credits
PQ: CMSC 25010. This course introduces some of the fundamental concepts in computer vision. We cover selected topics in low-level image analysis, perceptual grouping, three-dimensional reconstruction, motion estimation, and object recognition. Our approach is to focus on mathematical models and efficient algorithms. Spring.
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3.00 Credits
PQ: CMSC 25010. This course covers methods and models for computer vision. Topics include segmentation and feature detection. Y. Amit. Winter.
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3.00 Credits
PQ: CMSC 15300, or placement into MATH 15100 or equivalent. This is a directed course in mathematical topics and techniques that is a prerequisite for courses such as CMSC 2720 and 27400. We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Basic counting is a recurring theme and provides the most important source for sequences, which is another recurring theme. Further topics include proof by induction; recurrences and Fibonacci numbers; graph theory and trees; number theory, congruences, and Fermat's little theorem; counting, factorials, and binomial coefficients; combinatorial probability; random variables, expected value, and variance; and limits of sequences, asymtotic equality, and rates of growth. Autumn.
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3.00 Credits
PQ: CMSC 27100 or consent of instructor. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Algorithmic questions include sorting and searching, discrete optimization, algorithmic graph theory, algorithmic number theory, and cryptography. Design techniques include "divide-and-conquer" methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, the concepts of polynomial-time algorithms, and NP-completeness . Winter.
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