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. Instructor: Nuno Vasconcelos n u n o @ e c e . u c s d . e d u, EBU1-5602 TA: See piazza course page Text: Pattern Classification (2nd ed.) R. Duda, P. Hart, and D. Stork, Wiley Interscience, 2000 Syllabus: [ pdf] Homework: Problem set 1 [pdf,  data, intro slides] Issued: Lecture 4, Due: Lecture 6 Problem set 2 [pdf,  data]Issued: Lecture 6, Due: Lecture 8 Problem set 3 [pdf,  data]Issued: Lecture 8, Due: Lecture 10 (nothing to hand in) Problem set 4 [pdf]Issued: Lecture 12, Due: Lecture 16 Problem set 5 [pdf]Issued: Lecture 16, Due: Lecture 20 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: Maximum-likelihood 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: mid-term review [pdf] Lecture 11: mid-term Lecture 12: Conjugate and non-informative priors [slides] Lecture 13: Conjugate and non-informative priors [slides] Lecture 14: Kernel-based density estimates (DHS, chapter 4) [slides] Lecture 15: Mixture models [slides] Lecture 16: Expectation-maximization [slides] Lecture 17: Expectation-maximization [slides] Lecture 18: Expectation-maximization [slides] Lecture 19: Final review [pdf] Lecture 20: TBA