20134311: Introduction to Machine Learning

Winter 2013

Instructor: Greg Shakhnarovich, greg@ttic.edu
Monday, 1400-1600, Ziskind, Room 1  note change in location
Tuesday, 0900-1100, Ziskind, Room 1
First lecture: Monday December 31, 2012

Please fill out this online form as soon as possible

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 some books could be very helpful:
C. M. Bishop
Pattern Recognition and Machine Learning
T. Hastie, R. Tibshirani and J. H. Friedman
The Elements of Statistical Learning
(available online)
K. P. Murphy
Machine Learning: a Probabilistic Perspective
a new book


The Matrix Cookbook

Course calendar

Lecture slides, handouts, homework assignments, etc. Note: All handouts and lecture slides are in PDF format. Any scheduling information posted for future dates should be treated as tentative.