Machine Learning

10-601, Spring 2015

Carnegie Mellon University

Tom Mitchell and Maria-Florina Balcan



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Time:
Monday and Wednesday from 12:00-1:20pm (WEH 7500) (Recitations start on Jan 15)

Recitations:
Thursday 7:00-8:00pm (Doherty 2315)

Piazza Webpage:
https://piazza.com/class/i4mskrc82yo1pi

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)
  • Machine Learning: A Probabilistic Perspective, Kevin P. Murphy. (optional)
Grading:
  • Midterm # 1(25%)
  • Homeworks (30%)
  • Final project (20%)
  • Midterm # 2 (25%)
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.