• Instructor: Carlotta Domeniconi, Rm 449 ST2, carlotta@ise.gmu.edu

• Prerequisites: CS 681 or CS 687 or CS 688 or permission of instructor. Some programming experience is expected. Students should be familiar with basic probability and statistics concepts, linear algebra, optimization, and multivariate calculus.

• Time: We meet in IH 206, R 7:20pm - 10:00pm

• Textbook:

  C. M. Bishop Pattern Recognition and Machine Learning, Springer, 2006.
  Book's companion website
  

• Useful material

  Overview on Linear Algebra
  

General Description and Preliminary List of Topics:
Machine learning is concerned with the design of computer programs that can improve their performance based on experience. The course covers key algorithms and theory at the core of machine learning. Particular emphasis will be given to the statistical learning aspects of the field. Topics include: decision theory, Bayesian theory, curse of dimensionality, linear and non-linear dimensionality reduction techniques, classification, nearest neighbor methods, decision trees, clustering, kernel methods, ensemble methods, semi-supervised learning.

Course Format:
Lectures by the instructor. Besides material from the textbook, topics not discussed in the book may also be covered. Research papers and handouts of material not covered in the book will be made available. Grading will be based on homework assignments, exams, and a project. Homeworks will require some programming.