20114221: Introduction to Statistical Machine Learning

Winter 2012

Instructor: Greg Shakhnarovich, greg@ttic.edu
Tuesday, 0900-1100, Ziskind, Faculty Room (1st floor)  note change in location
Thursday, 0900-1100, FGS, Rm C
First lecture: Thursday Dec 29, 2011

Scope

This courses covers supervised learning, primarily classification and regreession. Some topics in unsupervised learning that are central to supervised tasks, such as density estimation, are covered as well. The emphasis is on the fundamental principles in design of statistical methods, such as bias-variance tradeoff and overfitting, and on computational issues in applying these methods to real data sets. In the process of eploring these fundamental topics the course covers in detail modern machine learning tools: generative and discriminative regression and classification models, support vector machines, decision trees, neural networks, ensemble methods etc. There are no formal prerequisites, but working knowledge of linear algebra and basic probability are necessary.

Books

There is no official textbook for the class, however the following books will be very helpful:
C. M. Bishop
Pattern Recognition and Machine Learning
T. Hastie, R. Tibshirani and J. H. Friedman
The Elements of Statistical Learning
(the most recent edition is available online)

Syllabus, lecture slides, homework assignments, etc.

Note: All handouts and lecture slides are in PDF format. Any information posted for future dates should be treated as tentative.

  • Thursday Dec 29
  • Tuesday Jan 3
  • Thursday Jan 5
  • Thursday Jan 5
  • Tuesday Jan 10
  • Thursday Jan 12
  • Tuesday Jan 17
  • Tuesday Jan 17
  • Thursday Jan 19
  • Tuesday Jan 24
  • Thursday Jan 26
  • Monday January 30
  • Tuesday January 31
  • Thursday February 2
  • Final exam: Tuesday February 21, 9:00-12:00
    Feinberg, room TBA