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Course Criteria
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3.00 Credits
PQ: Ability to program; and familiarity with elementary numerical methods and modeling physical systems by systems of differential equations. This course covers numerical methods for the solution of fluid flow problems. We also make a theoretical evaluation of the methods and experimental study based on the opinionated book Fundamentals of Computational Fluid Dynamics by Patrick J. Roache. T. Dupont. Spring.
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3.00 Credits
PQ: Graduate-level understanding of Unix/Linux operating systems, networking, computer architecture, and programming. This course introduces the concepts and technologies for building embedded systems and wireless sensors nets by focusing on four areas: low-power hardware, wireless networking, embedded operating systems, and sensors. Two assignments provide hands-on experience by deploying small wireless sensor motes running TinyOS to form an ad-hoc peer-to-peer network that can collect environmental data and forward it back to an 802.11b-equiped embedded Linux module. Students also read and summarize papers, participate in classroom discussions, and work on a team research project. R. Stevens. Not offered 2009 C10; will be offered 201 0 -11.
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3.00 Credits
PQ: CMSC 25000. This course is an introduction to the theoretical, technical, and philosophical aspects of Artificial Intelligence. The emphasis is on 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 reasoning and search. P. Niyogi. Winter.
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3.00 Credits
PQ: CMSC 25000/35000 or consent of instructor. This course is an introduction to the theory and practice of natural language processing, with applications to both text and speech. Topics include regular expressions, finite state automata, morphology, part of speech tagging, context free grammars, parsing, semantics, discourse, and dialogue. Symbolic and probabilistic models are presented. Techniques for automatic acquisition of linguistic knowledge are emphasized. Spring. Not offered 2009 C10; will be offered 201 0 -11.
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3.00 Credits
PQ: CMSC 25000/35000 or consent of instructor. This course is an introduction to the theory and practice of machine learning that emphasizes statistical approaches to the problem. Topics include pattern recognition, empirical risk minimization and the Vapnik Chervonenkis theory, neural networks, decision trees, genetic algorithms, unsupervised learning, and multiple classifiers. P. Niyogi. Spring.
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3.00 Credits
PQ: Consent of instructor. This course covers deformable models for detecting objects in images. Topics include one-dimensional models to identify object contours and boundaries; two-dimensional models for image matching; and sparse models for efficient detection of objects in complex scenes. Mathematical tools needed to define the models and associated algorithms are developed. Applications include detecting contours in medical images, matching brains, and detecting faces in images. Neural network implementations of some of the algorithms are presented, and connections to the functions of the biological visual system are discussed. Y. Amit. Winter.
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3.00 Credits
PQ: Consent of instructor. This course covers topics in artificial intelligence. Autumn, Winter, Spring.
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3.00 Credits
PQ: Linear algebra, finite fields, and a first course in group theory (Jordan-Holder and Sylow theorems) required; prior knowledge of algorithms not required. We consider the asymptotic complexity of some of the basic problems of computational group theory. The course demonstrates the relevance of a mix of mathematical techniques, ranging from combinatorial ideas, the elements of probability theory, and elementary group theory, to the theories of rapidly mixing Markov chains, applications of simply stated consequences of the Classification of Finite Simple Groups (CFSG), and, occasionally, detailed information about finite simple groups. No programming problems are assigned. L. Babai. Spring. Not offered 2009 C10; will be offered 201 0 -11.
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3.00 Credits
PQ: CMSC 27200 or consent of instructor. The focus of this course is the analysis and design 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 . L. Babai. Winter.
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3.00 Credits
PQ: CMSC 27200 or consent of instructor. This course discusses current topics in algorithms. Autumn, Winter, Spring.
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