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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, 12:30-1:50 PM, CENTR 113 | |
| 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: | TBA | |
| TBA | ||
| office hours: TBA | ||
| 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 4, Due: October 11 | |
| Problem set 2 [ps, pdf, data] Issued: October 11, Due: October 18 | ||
| Problem set 3 [ps, pdf,
data] Issued: November 1, Due: November 8 | ||
| Problem set 4 [ps, pdf] Issued: November 8, Due: November 15 | ||
| Problem set 5 [ps, pdf] Issued: November 15, Due: December 4 | ||
| 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: TBA | ||