Relevant Links

Class Material

• Syllabus Syllabus, Text books, Grading Scheme
• Class Notes for first 3 lectures: Introduction to Machine Learning, Chapter 1 from Alpaydin and Chapter 1 from Mitchell
• Class Notes for Introduction to Supervised Learning, Chapter 2 from Alpaydin
• Least-squares Regression: Chapter 17 from Numerical Methods for Engineers by Chapra and Canale. This is a very gentle introduction to regression. For more advanced material, look at any advanced text in Statistics.
• Decision and Regression Trees: Chapter 3 of Mitchell, Chapter 9 of Alpaydin, and Chapter 14 of Jang, Sun and Mizutani: Neuro-Fuzzy and Soft Computing. A handout on how to measure the accuracy of a classification experiment.
• Clustering Algorithms, Expectation Maximization Algorithm: Chapter 5 of Margaret Dunham's Data Mining: Introductory and Advanced Topics, and Chapter 7 of Alpaydin. Probablistic Clustering Algorithms: Chapter 6 of Data Mining by Witten and Frank, pages 218-225 in addition to Alpaydin.
• Chapter 6 of a manuscript by Coolidge on Correlation and Regression.
• Reinforcement Learning: Chapter 18 of Alpaydin, and Chapter 13 of Mitchell
• Naive Bayes Classifier: Section 6.9 of Mitchell, and Section 16.3.1 of Mitchell. A document written by me in 2002 when I worked for a local company--there are some errors in this document, but I had just a pre-final copy of the document when I left the company. If you refer to this document, please refer to it as "Kalita, Jugal K. 2002. Naive Bayes Classifiers for Spam Detection, University of Colorado at Colorado Springs".
• Principal Component Analysis: Chapter 6.3 of Alpaydin, a tutorial by Lindsay Smith
• Linear Classifiers and SVMs

Home Work Assignments

• Assignment 1: Decision Tree Learning
• Assignment 2: Clustering