Machine Learning

10-601, Fall 2011

Carnegie Mellon University

Tom Mitchell, Aarti Singh


Date and Time: Tuesdays and Thursdays, 1:30 - 2:50 pm
Location: Wean Hall 7500

Recitation: Date and Time: Wednesdays 6-7 PM
Location: Wean Hall 7500

  • Final project reports will be due December 9th at the beginning of the project poster session which will be from 12:30 - 3:30 PM in the University Center Connan Room.
  • A permanent time for recitation has been set. Recitations will be from 6-7 PM on Wednesdays in Wean Hall 7500.
  • We will be using Blackboard for grading and class discussion.
  • The class mailing list is If you wish to email only the instructors, the email is 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 10601-announce list page.
  • Exams : You are responsible for being in town during the midterm and final exam, in order to pass the course. With a class this size we cannot schedule individual exam times. The class midterm will be October 27. The final exam will be December 16, 5:30-8:30pm in Wean 7500.

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 and unsupervised 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: Textbooks are listed below. We will also provide online handouts and video lectures for certain topics, available on the Lectures tab above.
  • 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.