



This course provides an introduction to pattern recognition and statistical learning. Topics covered include: Bayesian decision theory; parameter estimation; maximum likelihood; the biasvariance tradeoff; Bayesian parameter estimation; the predictive distribution; conjugate and noninformative priors; dimensionality and dimensionality reduction; principal component analysis; Fisher's linear discriminant analysis; density estimation: parametric vs. kernelbased methods; mixture models; expectationmaximization; applications. 

Lectures:  MW, 12:302:00 PM, GH260  
Instructor:  Nuno Vasconcelos  
n u n o @ e c e . u c s d . e d u, EBU15603  
office hours: Wed 9:3011:00 AM  
TA:  Sunhyoung Han  
s 1 h a n @ u c s d . e d u, EBU15706  
office hours: Fri 10:3011:59 AM  
Text:  Pattern Classification (2nd ed.)  
R. Duda, P. Hart, and D. Stork, Wiley Interscience, 2000  
Syllabus:  [ps, pdf]  
Homework:  Problem set 1 [ps, pdf, data, intro slides]  
Problem set 2 [ps, pdf, data]  
Problem set 3 [ps, pdf, data]  
Problem set 4 [ps, pdf]  
Problem set 5 [ps, pdf]  
Cheetah Day:  Friday, Dec 5, 910:30AM, GH260  
Style: Example paper [pdf]  
LaTeX/Word Templates (gzipped tar file)  
LaTeX/Word Templates (zip file):  
Readings:  Lecture 1: introduction (DHS, chapter 1) [video]  
Lecture 2: Bayesian decision theory (DHS, chapter 2) [slides,video]  
Lecture 3: Bayesian decision theory (DHS, chapter 2) [slides,video]  
Lecture 4: Gaussian classifier (DHS, chapter 2) [slides,video]  
Lecture 5: Gaussian classifier (DHS, chapter 2) [slides,video]  
Lecture 6: Maximumlikelihood estimation (DHS, chapter 3) [slides,video]  
Lecture 7: Bias and variance (DHS, chapter 9) [slides,video]  
Lecture 8: midterm review [pdf]  
Lecture 9: midterm  
Lecture 10: Bayesian parameter estimation (DHS, chapter 3) [slides,video]  
Lecture 11: Bayesian parameter estimation (DHS, chapter 3) [slides,video]  
Lecture 12: Conjugate and noninformative priors [slides,video]  
Lecture 13: Conjugate and noninformative priors [slides,video]  
Lecture 14: Kernelbased density estimates (DHS, chapter 4) [slides,video]  
Lecture 15: Mixture models [slides,video]  
Lecture 16: Expectationmaximization [slides,video]  
Lecture 17: Expectationmaximization [slides,video]  
Lecture 18: Expectationmaximization [slides,video]  
Lecture 19: Final review [pdf]  
Lecture 20: Cheetah Day 