Machine Learning

CS60050  - Spring 2006
 

Syllabus      Schedule      Assignments     Resources     Links 
Time and Place
CS107
 First lecture: January 4, 2006
  • Monday: 9:30 to 10:25
  • Wednesday: 7:30 to 9:25
  • Thursday: 9:30 to 10:25
Instructor

Sudeshna Sarkar
Teaching Assistant

Sandip Aine
Textbook- Required

Indtroduction to Machine Learning - Ethem Alpaydin, MIT Press, Oct 2004; Prentice Hall of India, 2005
Other good books

  1. Machine Learning - Tom Mitchell, 1997
  2. Pattern Classification - Duda, Hart and Stork, 2000
  3. The Elements of Statistical Learning - Hastie, Tibshirani, Friedman, Springer 2001
Syllabus

Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, or to detect fraud in credit card transactions. The field of Machine Learning, which addresses the challenge of producing machines that can learn, has become an extremely active, and exciting area, with an ever expanding inventory of  practical (and profitable) results, many enabled by recent advances in the underlying theory.

This course will introduce the fundamental set of techniques and algorithms that constitute machine learning, ranging from classification methods like decision trees and support vector machines, over sequence models like hidden Markov models, to unsupervised learning and clustering. The course will not only discuss algorithms and methods, but also provide an introduction to the theory of machine learning. The course will cover some of the following topics. I will make additions/modifications in the course of the term.

  • Concept Learning: Version space, generalization ordering
  • Decision Trees
  • Hypothesis Tests: Confidence intervals
  • Linear Rules: Perceptron
  • Support Vector Machines: Optimal hyperplane, Kernels
  • Generative Models: Bayes Rule, Naïve Bayes, MAP and Bayesian learning
  • Hidden Markov Models: Viterbi, Expectation-Maximization
  • Nearest Neighbor: K-NN, asymptotics
  • Learning Theory: PAC learning, No-Free-Lunch
  • Clustering: HAC, k-means, latent semantic indexing
  • Reinforcement Learning: Q-Learning, Temporal difference learning
Resources

 
Reference Matrials Used

 
Homework Assignments

 
 
Grading