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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. | ||||
| 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 | ||||