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: MW, 12:30-1:50 PM, Peter 104




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: Vijay Mahadevan, Hamed Masnadi-Shirazi


{v m a h a d e v, h m a s n a d i} @ u c s d . e d u, EBU1-5512


office hours: Mon 5-6pm (EBU1-5512), Fri 3-4pm (EBU1-5101)




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]


Problem set 2 [ps, pdf data]


Problem set 3 [ps, pdfdata]


Problem set 4 [ps, pdf]


Problem set 5 [ps, pdf]







Cheetah Day: TBA


 Style: Example paper [pdf]


 LaTeX/Word Templates (gzipped tar file)


 LaTeX/Word Templates (zip file):




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: Cheetah Day