Course Description

Machine learning studies the question "how can we build computer programs that automatically improve their performance through experience?"   This includes learning to perform many types of tasks based on many types of experience.  For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you.  This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from optimization theory, and from information theory. Some recent advances such as large margin structured input/out learning, nonparametric Bayesian techniques based on Dirichlet process, etc., and instances of applications in datamining, genetics, and social sicences, will also be discussed.

Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong mathmatical background can catch up and fully participate.



The requirements of this course consist of participating in lectures,.... This is a PhD level class, and the most important thing for us is that by the end of this class students understand the general and some advanced methodologies in machine learning, and be able to use them to solve real problems of modest complexity. The grading breakdown is the following:

  • Exam (100%)
  • Final project (optional)


Projects and collaboration policy

The final project is optional. But we encourage those students who are interested in doing some real practice to choose. The project may be completed by small teams. The instructor and TAs are available to help.

© 2009 Eric Xing @ School of Computer Science, Carnegie Mellon University