MW 9:30-10:50 Am, Fall 2010,
Math Science 5128
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.
Math 33A Linear Algebra and Its Applications, Matrix Analysis
Stat 100B Intro to Mathematical Statistics,
CS 180 Intro to Algorithms and Complexity.
R. Duda, P. Hart, D. Stork, "
", 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
", Spinger Series in Statistics, 2001. [Reference]
N. Cristianini and J. Shawe-Taylor, "
An Introduction to Support Vector Machines