 

 
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:  TuTh, 12:301:50 PM, CSB 002  
Instructor:  Nuno Vasconcelos  
n u n o @ e c e . u c s d . e d u, EBU15602  
office hours: Friday 9:3010:30AM  
TA:  Mandar Dixit  
office hours: Mon. 3:00pm  4:30pm @ EBU14600  
Weixin Li  
office hours: Wed. 12:00pm  1:30pm @ EBU14600  
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] Issued: October 1, Due: October 15  
Problem set 2 [ps, pdf, data] Issued: October 15, Due: October 22  
Problem set 3 [ps, pdf,
data] Issued: October 22, Due: November 5  
Problem set 4 [ps, pdf] Issued: November 5, Due: November 24  
Problem set 5 [ps, pdf] Issued: November 24, Due: December 3  
Note: all dates are tentative.  
Readings:  Lecture 1: introduction (DHS, chapter 1)  
Lecture 2: Bayesian decision theory (DHS, chapter 2) [slides]  
Lecture 3: Bayesian decision theory (DHS, chapter 2) [slides]  
Lecture 4: Gaussian classifier (DHS, chapter 2) [slides]  
Lecture 5: Gaussian classifier (DHS, chapter 2) [slides]  
Lecture 6: Maximumlikelihood estimation (DHS, chapter 3) [slides]  
Lecture 7: Bias and variance (DHS, chapter 9) [slides]  
Lecture 8: Bayesian parameter estimation (DHS, chapter 3) [slides]  
Lecture 9: Bayesian parameter estimation (DHS, chapter 3) [slides]  
Lecture 10: midterm review [pdf]  
Lecture 11: midterm  
Lecture 12: Conjugate and noninformative priors [slides]  
Lecture 13: Conjugate and noninformative priors [slides]  
Lecture 14: Kernelbased density estimates (DHS, chapter 4) [slides]  
Lecture 15: Mixture models [slides]  
Lecture 16: Expectationmaximization [slides]  
Lecture 17: Expectationmaximization [slides]  
Lecture 18: Expectationmaximization [slides]  
Lecture 19: Final review [pdf]  
Lecture 20: TBA 