Machine Learning

10-701/15-781, Spring 2011

Carnegie Mellon University

Tom Mitchell

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VIDEO LECTURES:
Videos of class lectures are available, along with lectures slides, homeworks, and exams. These are available to everyone for personal use, free of charge. I hope you find these useful.


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:
  • Machine Learning, Tom Mitchell. (optional)
  • Pattern Recognition and Machine Learning, Christopher Bishop. (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.