Department of Computer and Information Science

 

ENGR 691/2 Section 66 - Machine Learning

Location: Weir 235
Time: 14:00 - 14:50, MWF
Instructor: Prof. Yixin Chen, ychen@cs.olemiss.edu

Course Goal
 This course seeks to introduce to the students the basic theory, concepts, and techniques of machine learning and give them a glimpse in the state-of-the-art of the area. The students will attain knowledge and skill of converting a machine learning algorithm discussed in the class to a computer program for a real world application.

Prerequisite
 Basic knowledge of statistics and linear algebra; good programming skills in C, or C++, or JAVA, or Matlab, or other programming languages.

Textbook
 Duda, Hart, and Stork, Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2001.
A collection of additional readings from journals and conference proceedings will be handed out.

Topics

  • Bayesian decision theory
  • Maximum-likelihood estimation
  • Bayesian estimation
  • Nonparametric techniques
  • Linear discriminant analysis
  • Computational learning theory
  • Support vector machines and kernel methods
  • Boosting
  • Clustering
  • Dimensional reduction

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