CS 5860:Machine Learning
Instructor: Jugal Kalita
- Syllabus Syllabus, Text books, Grading
- Introduction to Machine Learning: Chapter 1 of Alpaydin
- Supervised Learning:
Chapter 2 of Alpaydin
- Regression: We looked at Chapter 17, "Least-Squares Regressio"n from Numerical Methods for Engineers by Chapra and Canale; Also Sections 2.6 and 5.8 Alpaydin
- Decision Trees: We covered Chapter 3 of Machine Learning by Mitchell; Also Chapter 9 of Alpaydin
- k-NN classification (Sections 8.4 and 8.5 of Alpaydin) and Instance-based Learning: We read a Technical Report by Cunningham and Delany called "k-Nearest Neigbour Classifiers" and a paper titled "Instance-Based Learning Algorithms" by Aha, Kilber and Albert, Machine Learning, 6, 37-66, 1991.
- Naive Bayes Classifiers: We looked at Chapter 2 from Beaux Sharifi's MS thesis;
Kalita's writeup on an experiment using Naive Bayes spam classification from 2002.
- Bayesian Networks: Chapter 16 from Alpaydin. We also looked at Basics (Chapter 2, "Introducing Bayesian Networks" from Bayesian Artificial Intelligence by Korb), Reasoning in Bayesian Networks (Chapter 3, "Inference inBayesian Networks" from Bayesian Artificial Intelligence by Korb and Chapter 15, "Probabilistic Reasoning Systems" from Artificial Intelligence: A Modern Approach by Russell and Norvig), Learning BN parameters, Learning BN stuctures.
- Support Vector Machines
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.