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.




Lectures: TuTh, 12:30-1:50 PM, WLH 2111




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: Weixin Li (wel017 at ucsd.edu)


office hours: Monday 4:00pm - 5:30pm, EBU1-5101




Song Lu (sol050 at eng.ucsd.edu)


office hours: Wednesday 3:10pm - 4:40pm, EBU1-5512




Text: Pattern Classification (2nd ed.)


R. Duda, P. Hart, and D. Stork, Wiley Interscience, 2000



Syllabus: [ps, pdf]



Homework: Problem set 1 [ps, pdfdata, intro slides]
Issued: October 3, Due: October 15


Problem set 2 [ps, pdf data]
Issued: October 15, Due: October 22


Problem set 3 [ps, pdfdata]
Issued: October 22, Due: November 14


Problem set 4 [ps, pdf]
Issued: November 14, Due: November 26


Problem set 5 [ps, pdf]
Issued: November 26, Due: December 5
Note: all dates are tentative.



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: Bayesian parameter estimation (DHS, chapter 3) [slides,video]


Lecture 9: Bayesian parameter estimation (DHS, chapter 3) [slides,video]


Lecture 10: mid-term review [pdf]


Lecture 11: mid-term


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