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 [pdfdata, intro slides]
Issued: Lecture 4, Due: Lecture 6


Problem set 2 [pdf data]
Issued: Lecture 6, Due: Lecture 8


Problem set 3 [pdfdata]
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