271A -
Statistical Learning I

 

 

             

This course provides an introduction to pattern recognition and statistical learning. Topics covered include: Bayesian decision theory; parameter estimation; maximum likelihood; the bias-variance trade-off; Bayesian parameter estimation; the predictive distribution; conjugate and non-informative priors; dimensionality and dimensionality reduction; principal component analysis; Fisher's linear discriminant analysis; density estimation: parametric vs. kernel-based methods; mixture models; expectation-maximization; applications.

Lectures: MW, 12:30-2:00 PM, GH260
Instructor: Nuno Vasconcelos
n u n o @ e c e . u c s d . e d u, EBU1-5603
office hours: Wed 9:30-11:00 AM
TA: Sunhyoung Han
s 1 h a n @ u c s d . e d u, EBU1-5706
office hours: Fri 10:30-11:59 AM
Text: Pattern Classification (2nd ed.)
R. Duda, P. Hart, and D. Stork, Wiley Interscience, 2000
Syllabus: [ps, pdf]
Homework: Problem set 1 [ps, pdfdata, intro slides]
Problem set 2 [ps, pdfdata]
Problem set 3 [ps, pdfdata]
Problem set 4 [ps, pdf]
Problem set 5 [ps, pdf]
Cheetah Day:  Friday, Dec 5, 9-10:30AM, 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: Maximum-likelihood estimation (DHS, chapter 3) [slides,video]
Lecture 7: Bias and variance (DHS, chapter 9) [slides,video]
Lecture 8: mid-term review [pdf]
Lecture 9: mid-term
Lecture 10: Bayesian parameter estimation (DHS, chapter 3) [slides,video]
Lecture 11: Bayesian parameter estimation (DHS, chapter 3) [slides,video]
Lecture 12: Conjugate and non-informative priors [slides,video]
Lecture 13: Conjugate and non-informative priors [slides,video]
Lecture 14: Kernel-based density estimates (DHS, chapter 4) [slides,video]
Lecture 15: Mixture models [slides,video]
Lecture 16: Expectation-maximization [slides,video]
Lecture 17: Expectation-maximization [slides,video]
Lecture 18: Expectation-maximization [slides,video]
Lecture 19: Final review [pdf]
Lecture 20: Cheetah Day