|
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: | TuTh, 3:30-5:00, HSS 1128A | |
| Instructor: | Nuno Vasconcelos | |
| n u n o @ e c e . u c s d . e d u, EBU1-5603 | ||
| office hours: Wed 3-4:30 PM | ||
| 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] | |
| Problem set 2 [ps, pdf, data] | ||
| Problem set 3 [ps, pdf, data] | ||
| Problem set 4 [ps, pdf] | ||
| Problem set 5 [ps, pdf] | ||
| Problem set 6 [ps, pdf] | ||
| Cheetah Day: | [ps, pdf] | |
| Readings: | Lecture 1: introduction (DHS, chapter 1) | |
| Lecture 2: Bayesian decision theory (DHS, chapter 2) | ||
| Lecture 3: Bayesian decision theory (DHS, chapter 2) | ||
| Lecture 4: Gaussian classifier (DHS, chapter 2) | ||
| Lecture 5: Maximum-likelihood estimation (DHS, chapter 3) | ||
| Lecture 6: Bias and variance (DHS, chapter 9) | ||
| Lecture 7: Bias and variance (DHS, chapter 9) | ||
| Lecture 8: Bayesian parameter estimation (DHS, chapter 3) | ||
| Lecture 9: Bayesian parameter estimation (DHS, chapter 3) | ||
| Lecture 10: Conjugate and non-informative priors | ||
| Lecture 11: Dimensionality (DHS, chapter 3) [slides] | ||
| Lecture 12: Dimensionality reduction (DHS, chapter 3) | ||
| Lecture 13: Nearest neighbor classifier (DHS, chapter 4) | ||
| Lecture 14: mid-term | ||
| Lecture 15: Kernel-based density estimates (DHS, chapter 4) [slides] | ||
| Lecture 16: Mixture models [slides] | ||
| Lecture 17: Expectation-maximization [slides] | ||
| Lecture 18: Expectation-maximization | ||
| Lecture 19: Applications [slides] | ||