Machine Learning

10-701/15-781, Fall 2006

Eric Xing and Tom Mitchell
School of Computer Science, Carnegie-Mellon University






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The class mailing list is 10701-fall06@cs. If you wish to email only the instructors, the email is 10701-fall06-instructors@cs.

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 Bayesian statistics and from information theory.

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 numerate background can catch up and fully participate.

If you are interested in this topic, but are not a PhD student, you might consider the master's level course on Machine Learning, 15-681.

Page links


Web pages for earlier versions of this course:  (include examples of midterms, homework questions, ...)