
Lecture  Topic  Reading/Assignments/Notes  Due Date 
L1  Introduction to Machine Learning 
Read the introductory chapter of [M], [WF], [F] or [A]
See also lecture slides. 

Supervised Learning Basics:  
L2  Instance based learning 
[M] Chapter 8 is cloest to class material;
or [RN] 18.8; or [DHS] 4.44.6.
See also lecture slides. See also Andrew Moore's tutorial on kdtrees See also original paper describing the Relief Method 

L34  Decision Trees 
[M] Chapter 3;
or [RN] 18.14; or [F] Chapter 5.
See also lecture slides. 

Optional Reading  T. Dietterich, M. Kearns, and Y. Mansour Decision Tree Learning and Boosting Applying the Weak Learning Framework to Understand and Improve C4.5. International Conference on Machine Learning, 1996.  
Written Assignment 1  Assignment 1  9/24  
Empirical/Programming Assignment 1  Project 1 and corresponding Data  9/29  
L5  Naive Bayes Algorithm 
[M] 6.16.2, and 6.96.10;
[DHS] Section 2.9;
[F] 9.2; [WF] 4.2.
See also new book chapter from [M] See also lecture slides. Lecture also provided a basic introduction to probability and working with random variables. 

L67  Evaluating Machine Learning Outcomes 
[M] Ch 5; [F] Ch 12
See also lecture slides. 

Optional Reading 
Foster Provost, Tom Fawcett, Ron Kohavi
The Case Against Accuracy Estimation for Comparing Induction
Algorithms
Proc. 15th International Conf. on
Machine Learning, 1998.
T. Dietterich, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms Neural Computation 10(7), 1998. Stephen Salzberg On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach Data Mining and Knowledge Discovery, 1997. 

L78  Features (selection, transformation, discretization) 
Relevant reading includes some portions of [F] Chapter 10, and [A]
Chapter 6
(there is a good overlap but not a perfect match)
See also lecture slides. 

Optional Reading 
Wrappers for Feature Subset Selection
Ron Kohavi, George H. John
Artificial Intelligence, 1996.
(Read till section 3.2 inclusive.)
Supervised and unsupervised discretization of continuous features. James Dougherty, Ron Kohavi, and Mehran Sahami. International Conference on Machine Learning, 1995. 

Written Assignment 2  Assignment 2  10/8  
Empirical/Programming Assignment 2  Project 2 and corresponding Data  10/13 