INLS-110-40
– Machine Learning
Spring 2002, MW 8-9:15, Room 208
UNC-CH SILS
Bob Losee, Manning 302
962-7150, losee@ils.unc.edu
Learning algorithms that can be implemented on computers. Emphasis will be on statistical methods for both supervised and unsupervised learning, pattern recognition, and clustering. Non-statistical methods will be discussed and compared to statistical methods
INLS 150 and 172 are recommended although not required. While there is no mathematical prerequisite, students should anticipate reading materials describing a variety of mathematical techniques consistent with a variety of mathematical paradigms. Math phobic students will be uncomfortable in this course; students are not expected to understand everything or even most that they read, but are expected to appreciate the different approaches.
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. The Elements of Statistical Learning, Springer, 2001. (Denoted below as HTF)
Students will find it beneficial to obtain a flexible programming package (Mathematica or S) designed for statistical methods. Some Mathematica (produced by WRI) will be used in class and the book web site has some code in S. Mathematica is available for purchasing in the RAM shop, and is installed on PCs in our lab, as well as ruby, our unix workstation. The author will present some procedures in class using Mathematica. The author does not anticipate presenting anything in S (although it is a fine package)
|
Topic |
Readings |
|
Introduction
and Probability Basics |
HTF
Ch. 1 |
|
Supervised
Learning |
HTF
Ch. 2 |
|
Linear
Methods for Regression |
HTF
Ch. 3 |
|
Linear
Methods for Classification |
HTF
Ch. 4 |
Bayesian Methods |
Bishop
Ch. 1, 2.3 Theodoridis
Ch. 2.1-2.4 |
|
Specific
Distributions |
Pratt et al. Ch 9, 11. |
|
Basis
Expansion and Regularization |
HTF
Ch. 5 |
|
Kernel
Methods |
HTF
Ch. 6 |
|
Model
Assessment and Selection |
HTF
Ch. 7 |
|
Neural
Networks |
HTF
Ch. 11 Bishop
Ch. 3 |
|
Support
Vector Machines |
HTF
Ch. 12 |
|
Unsupervised
Learning |
HTF
Ch. 14 |
|
Evolutionary
and Genetic Algorithms |
|
|
Sequences
and HMMs |
Baldi
and Brunak, |
|
Learning
Game Strategies |
Poundstone,
Ch. 1, 12, 13. |
Students are expected to conduct four small studies using machine learning techniques. Students will develop one or more databases to be used for these projects.
The first project should implement a regression (without using the “regression” command found in the programming package the student is using.)
The second project should use neural networks
The third project should use unsupervised learning
The fourth project should use genetic algorithms, learning sequences, or game strategies
Class participation & homework 60%,
Projects 40%
Baldi, Pierre, and Brunak, Soren. Bioinformatics, Second Edition. MIT Press, 2001.
Bishop, Christopher. Neural Networks for Pattern Recognition. Oxford U. Press, 1995.
(HTF) Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. The Elements of Statistical Learning, Springer, 2001.
Poundstone, William. Prisoner’s Dilemma, Doubleday, 1992.
Pratt, John, Raiffa, Howard, and Schlaifer, Robert. Introduction to Statistical Decision Theory. MIT Press, 1995.
Theodoridis, Sergios and Koutroumbas, Konstantinos. Pattern Recognition. Academic Press, 1999.
Webb, Andrew. Statistical Pattern Recognition, Arnold Press and Oxford U. Press, 1999.
Students should familiarize themselves with the University of North Carolina at Chapel Hill Honor Code, which is described in University publications. It should be noted that in this course, students may receive (and provide) some assistance with general problem solving techniques and the readings. Students should NOT receive (or provide) major creative assistance on the class project.
Last updated: February
20, 2002