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: M 9th Period and W 8-9th Periods or by appointment. 

Grading:

1. Homeworks: 20%.
2. Midterms: 30%.
3. Project: 50%.

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 two midterms will be scheduled later.
   
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. Boosting methods and Ada-Boost.
2. Graphical models.
   
3. Manifold learning.
4. Variational methods for learning.