6.892 Machine learning seminar

Prof. Tommi Jaakkola
tommi@ai.mit.edu (preferred point of contact)
NE43-735, x3-0440

Meets: Mon/Wed 2.30-4pm, 26-322 (First class Wed Feb 3).


Statistical machine learning concerns with automated, formalized methods with the ability to adapt, infer or learn from experience for the purpose of prediction and decision making. The aim of this class is to provide the students with fundamentals of various machine learning techniques so that they can readily apply, analyze, or adjust existing methods. The emphasis will be on representational and computational issues. A wide range of topics will be covered such as representation of probabilities with graphs, inference and estimation on graphs, approximate methods, model selection, clustering, generalization.

  • Brief syllabus
  • Tentative schedule
  • Homework assignments (postscript files)
  • Homework 1
  • Homework 2
  • Homework 3
  • Homework 4
  • Homework 5
  • Homework 6
  • Homework 7
  • Homework 8
  • Homework 9
  • Handouts (postscript files)
  • Handout 1: simple node elimination
  • Handout 2: decimation
  • Handout 3: param. estimation in ud-models
  • Handout 4: EM and constrained maximization of convex functions
  • Handout 5: max entropy, mean field
  • Handout 6: beyond mean field