Course OutlineECE271A – Statistical Learning IDepartment of Electrical and Computer EngineeringUniversity of California, San DiegoNuno Vasconcelos Fall 2007Your responsibilities in this class fall into three main categories:1. Class participation and homework 20%2. Mid-term 30%3. Final 40%4. Cheetah Day 10%You are allowed to collaborate on homework as long as you write your solutions independently and acknowl-edge the collaboration in the problems where it was used. Homework will be handed-out on Thursdays andis due one week after the hand-out date. It will have a problem solving component and a component ofcomputer problems. I assume that students have access to Matlab. Let me know if that is a problem. Thecomputer problems will consist of the application of a number of techniques to a given problem (Cheetah).On the last day of classes we will get together for Cheetah Day, where everyone will present the big pictureconclusions.InstructorNuno Vasconcelos,EBU1 5603, 4-5550, e-mail: [email protected]ffice hours: Fridays 9:30-11:00AMTA TBAExam dates:• Mid-term - TBA• Final - finals weekTe x t : We will follow closely• Richard O. Duda, Peter E. Hart and David G. Stork Pattern Classification. New York, NY: JohnWiley&Sons, 2001.Supplementary hand-outs will be distributed when appropriate. There are various other books of interest.These are not required but can be used for alternative explanations of the material.1. T. Hastie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning. Springer Verlag, 2001.2. Luc Devroye, Laszlo Gyorfi, Gabor Lugosi, A Probabilistic Theory of Pattern Recognition. SpringerVerlag, 1998.3. Andrew Gelman, Donald B. Rubin, Hal S. Stern, Bayesian Data Analysis, Second Edition, CRC Press;2nd edition, 2003.4. Tom Mitchell, Machine Learning, McGraw-Hill, 1997.5. Christopher Bishop, Neural Networks for Pattern R ecognition. Oxford University Press, 1996.6. Vladimir Vapnik, The Nature of Statistic al Learning The ory. Springer Verlag, 1999.There is a web page for the course,http://www.svcl.ucsd.edu/courses/ece271A-F07/ece271A-F07.htm(also accessible from http://www.svcl.ucsd.edu/~nuno)LECTURE SUBJECT Number of classesIntroduction 1Bayesian decision theory 2The Gaussian classifier 1Maximum likelihood estimation 1Bias and variance 2Bayesian parameter estimation 2Conjugate and non-informative priors 1Dimensionality and dimensionality reduction 2The nearest neighbor classifier 1Kernel-based density estimation 1Mixture models and EM 3Applications