CSC 663 - Machine Learning

Course Objective

This course covers the theory and practice of machine learning from a variety of perspectives (including Design, analysis, implementation and applications of learning algorithms). The course covers theoretical concepts such as induction, deduction, reinforcement and interaction. Topics include learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning, support vector machines, decision trees, Bayesian networks, association rules, dimensionality reduction, feature selection and visualization. 

Grading Policy:

Assignments:    10%
Project:             40%
Presentation:     10%
Final Exam        40%

Textbook

• Ethem Alpaydin, Introduction to Machine Learning, Second Edition http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12012

Assignments

Projects

Resources

Class resources will be maintained on this web site. Projects will be submitted and grades will be maintained on LMS.

• Weka - Machine learning software you'll be using for some of your projects.
• UCI Machine Learning Repository - An online repository of data sets that can be used for machine learning experiments.

Lecture Schedule

Schedule subject to change.

Week	Topics	Reading
1	Introduction	Chapter 1
2	Supervised  Learning	Chapter 2
3	Baysian Decision Theory	Chapter 3
4	Nonparametric Methods	Chapter 8
5	Decision Trees Machine Learning Experiments Weka Tutorial	Chapter 9  Chapter 19
6	Linear Discrimination Tutorial	Chapter 10
7	Multilayer Perceptrons Face Recognition using NN	Chapter 11
8	Dimmensionality Reduction
9	Clustering
10	Kernal Machines
11	Combining Learners
12	Reinforcement Learning
Final Exam: