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, 12:30-1:50 PM, Peter 104 Instructor: Nuno Vasconcelos n u n o @ e c e . u c s d . e d u, EBU1-5602 office hours: Friday 9:30-10:30AM TA: Vijay Mahadevan, Hamed Masnadi-Shirazi {v m a h a d e v, h m a s n a d i} @ u c s d . e d u, EBU1-5512 office hours: Mon 5-6pm (EBU1-5512), Fri 3-4pm (EBU1-5101) 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: TBA 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