Machine Learning

10-601, Fall 2012

Carnegie Mellon University

Tom Mitchell and Ziv Bar-Joseph



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Time:
Tuesday and Thursday from 1:30-2:50pm (WEH 7500)

Recitations:
Tuesday from 5-6pm (NSH 1305), Wednesday from 5-6pm (PH 125C)

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. 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:
  • Grades will be based 40% on homeworks, 25% on the midterm, and 35% on the final exam.
  • Please also see our policy on late homeworks.
Auditing: At this stage, unfortunately we cannot allow audits. We do not have enough space for registered students so clearly we cannot accommodate any audits.