Machine Learning

10-701/15-781, Spring 2011

Carnegie Mellon University

Tom Mitchell

Previous material

Lecture:

Date and Time: Tuesdays and Thursdays 1:30 - 2:50 pm
Location: Margaret Morrison A14

Recitation:

Date and Time: Wednesdays, 5:00 - 6:00 pm, beginning Jan 19
Location: Newell-Simon 3305 (except Feb 16, in Gates 6115)

Announcements:

Final exam will be Friday, May 6, from 1-4pm, in Gates-Hillman 4401. Like the midterm, it is open book, open notes, no computer, no internet connection. Here is a short list of topics to study
Video lectures: We are creating videos of some of the class lectures, and these are available to you as additional study material. The entire list of lectures is here, and these are posted within an hour or two of lecture. For convenience, we have also placed links to individual lectures, along with copies of lecture slides, under the lectures tab above. To view a video you will have to login with your CMU Andrew username and password, as shown here. If you have no CMU Andrew ID, contact the instructors to arrange access.
The class mailing list is 10701-announce@mailman.srv.cs.cmu.edu. If you wish to email only the instructors, the email is 10701-instructors@mailman.srv.cs.cmu.edu . If you are registered for the course, you have automatically been added to the mail group. If you are for some reason NOT receiving these announcements, you can subscribe via the 10701-announce list page .

Course Description:

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. 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 machine learning.

Prerequisites:

Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. In addition, recitation sessions will be held to review some basic concepts.

Textbook:

Pattern Recognition and Machine Learning, Christopher Bishop.
Machine Learning, Tom Mitchell. (optional)
The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. (optional)

Grading:

Midterm (25%)
Homeworks (30%)
Final project (20%)
Final exam (25%)

Auditing:

To satisfy the auditing requirement, you must either:

Do *two* homeworks, and get at least 75% of the points in each; or
Take the final, and get at least 50% of the points; or
Do a class project
- Like any class project, it must address a topic related to machine learning and you must have started the project while taking this class (can't be something you did last semester). You will need to submit a project proposal with everyone else, and present a poster with everyone. You don't need to submit a milestone or final paper. You must get at least 80% on the poster presentation part of the project.

Please, send the instructors an email saying that you will be auditing the class and what you plan to do.