1. Required: Pattern Recognition and Machine Learning, Christopher M. Bishop, Publisher: Springer, 2007.
   
2. Other Material: Notes and papers from the research literature.

Office hours: MWF 4th period or by appointment. 

Grading:

1. Homeworks: 25%.
2. Midterms: 50%.
3. Project: 25%.

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: Matrix multiplication, inverse, pseudo-inverse. Probability theory: Conditional probability, Bayes rule, conditional expectations. Optimization: Gradient descent, expectation-maximization (EM). Machine Learning (CAP6610) is obviously a useful precursor to this course.
   
2. Homeworks/programs will be assigned on an ad-hoc basis. If you do not have any prior numerical computing experience, I suggest you use MATLAB for the programs.
3. The first midterm is scheduled for Wed. Oct. 24th from 8:20-10:10PM. The second midterm is scheduled for Wed. Dec 5th from 8:20-10:10PM.
   
4. The project is due at the end of the semester. Depending on the number of students, the project will be either in teams of two or individual.
5. A set of informal notes which will evolve with the course can be found here.
   

   
   

1. Manifold learning including local linear embedding, ISOMAP, Laplacian eigenmaps.
   
2. Graphical models including Markov random fields, message passing algorithms, Bethe and Kikuchi approximations, CCCP.
   
3. Boosting methods - AdaBoost and variants, random forests.
   
4. Dirichlet processes and non-parametric Bayesian methods.