CS5691: Pattern recognition and Machine learning
Course information
When: JanMay 2020
Lectures: Slot J
Where: CS25
Teaching Assistants: TBA
Course Content
Review of probability
Bayes decision theory and Bayes classifer
Maximum likelihood and Bayesian parameter estimation
Statistical learning theory
Linear models
Linear leastsquares regression, logistic regression, regularized least squares, biasvariance tradeoff
Dimensionality reduction
Support vector machines and kernel methods
Online learning
Mixture densities and EM algorithm
Kmeans clustering
Multilayer neural networks
Important Dates
Miniquizzes: Feb 3, Mar 9, Apr 13
QuizI: Feb 19, QuizII: Mar 18
Final: May 2
PAI: Available on Feb 20, submit by Mar 8
PAII: Available on Mar 20, submit by Apr 8
Contest: Apr 25
Textbooks
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, MIT Press, 2019
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, John Wiley, 2001
Additional References
Shai ShalevShwartz and Shai BenDavid, Understanding Machine Learning, Cambridge Univ. Press, 2014
Simon Haykin. Neural networks and learning machines, Pearson Education, 2009.
Bernhard Scholkopf, Alexander J. Smola, Learning with Kernels, MIT press, 2002.
Michael Kearns and Umesh Vazirani, An Introduction to Computational Learning Theory, MIT press, 1994.
