CS 586:Machine Learning
Instructor: Jugal Kalita
- Syllabus Syllabus, Text books, Grading
- 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
I will give you 2 or 3 home work assignments. They will invlove programs that learn with real or imagined data. Please make sure you finish all assignments before the final
and demo them to me.