stat_231_syllabus - Stat 231: Pattern Recognition and...

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MW 9:30-10:50 Am, Fall 2010, Math Science 5128 Course Description This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning, which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics. Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis, boosting techniques, kernel methods and support vector machine, and fast nearest neighbor indexing and hashing. Prerequisites Math 33A Linear Algebra and Its Applications, Matrix Analysis Stat 100B Intro to Mathematical Statistics, CS 180 Intro to Algorithms and Complexity. Textbook R. Duda, P. Hart, D. Stork, " Pattern Classification ", second edition, 2000. [Required] [ link to book page ] C.M. Bishop, " Pattern Recognition and Machine Learning ", Springer, 2006 [Reference] T. Hastie, R. Tibshurani, and J.H. Friedman, " The Elements of Statistical Learning: Data Mining, Inference, and Prediction ", Spinger Series in Statistics, 2001. [Reference] N. Cristianini and J. Shawe-Taylor, " An Introduction to Support Vector Machines
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This note was uploaded on 11/24/2010 for the course STAT 201a taught by Professor Wu during the Spring '10 term at Pasadena City College.

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stat_231_syllabus - Stat 231: Pattern Recognition and...

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