Machine Learning, 10-701 and 15-781

Tom M. Mitchell & Andrew W. Moore

School of Computer Science, Carnegie Mellon University

Fall 2002


It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem.

The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statististics and from statistical algorithmics.

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.

Class lectures: Tues & Thurs 10:30-11:50, Wean Hall 7500

Optional recitation section: Mondays 5:00-6:30 beginning Sept 23 Newell-Simon Hall 1305


Teaching Assistants:


Course Website (this page):


Policy on late homework:

Policy on collaboration:

Homework assignments

Lecture schedule (and online slides if available)

Here are some example questions for studying for the final. Note that these are exams from earlier years, and contain some topics that will not appear in this year's final. And some topics will apear this year that do not appear in the following examples.

Review Sessions (Mondays, 5pm-6.20pm, NSH 1305)

The "Review of topics to date" sessions will be run by Andrew or Tom bringing a bunch of questions from recent exams and using them as starting points to discuss things that have come up in class so far. Students will be very strongly encouraged to ask questions about anything that they feel they need to know more about or would like to know more about, or things that were done in class but which they'd like to see repeated in slow motion.

The assignment sessions will be similar, except preference will be given to questions relevant to understanding the assignment. In some cases the TA might show examples of solving questions that are similar to those on the assignment.

Note to people outside CMU

Feel free to use the slides and materials available online here. Please email the instructors with any corrections or improvements. Additional slides are available at the Machine Learning textbook homepage and at Andrew Moore's tutorials page. Past homework exercises and exams are available at Mitchell's Fall 1998 course homepage.