1. Required: Pattern Recognition and Machine Learning, Christopher M. Bishop, Publisher: Springer, 2007.
   
2. Additional: Statistical Learning Theory, Vladimir N. Vapnik, Publisher: John Wiley and Sons, New York, 1998.
   
3. Other Material: Notes and papers from the research literature.

Office hours: Anand: MWF 8^th period or by appointment.
Hang: T 8th period and R 8th and 9th periods in CSE E309.

Grading:

1. Homeworks: 20%.
2. Two Midterms: 20% each.
3. Project: 40%.

1. Prerequisites: A familiarity with basic concepts in calculus, linear algebra, and probability theory. A partial list of basic requirements follows. Calculus: Differentiation, chain rule, integration. Linear algebra: Vector spaces, inverse, pseudo-inverse. Probability theory: Conditional probability, Bayes rule, conditional expectations. A basic understanding of Hilbert spaces (from Math for Intelligent Systems - COT5615 or equivalent). While AI is listed as a pre-requisite, if any aspect of AI turns out to be required, it will be taught in class in order to make the course self-contained.
2. Homeworks/programs will be assigned bi-weekly. If you do not have any prior numerical computing experience, I suggest you use MATLAB for the programs.
3. The first midterm will probably be held on Wednesday, February 26^th, 2014 (from 8:20-10:10PM) and the second will probably be held on Monday, April 21^st, 2014 (from 8:20-10:10PM). Each midterm will be 1 hour and fifty minutes long.
   
4. The project is due at the end of the semester.
   

1. Introduction to supervised and unsupervised learning.
   
2. Fisher discriminants, linear regression and classification.
3. Introduction to convex optimization.
4. Kernel methods and support vector machines (SVMs).
5. Regression methods and sparse approximations.
   
6. Convexity, maximum likelihood principle.
7. Mixture models and Expectation-Maximization (EM) methods in clustering, K-means.
   
8. Component Analysis (PCA and ICA).
   
9. Introduction to manifold learning (if time permits).