COSC 388: Machine Learning

Course Description

This course surveys the major research areas of machine learning, concentrating on inductive learning. Topics will include rule induction, decision trees, neural networks, instance-based approaches, genetic algorithms, evaluation, and applications.

Undergraduate students must complete five programming projects using a language of their choosing. COSC 173 is a prerequisite.

Graduate students must complete an additional programming assignment on a topic of their choosing. It must involve either using or developing machine-learning software to address an appropriate problem. This will include an informal proposal, due during the early part of the semester, and a research report, due at the end of the term, describing the problem, the past work of others, the approach taken, and empirical results. The research report should be roughly equivalent to a conference paper.

Primary Texts:

• Machine learning, by Mitchell.

Other Resources:

• Elements of machine learning by Langley
• Data mining: Practical machine learning tools and techniques with Java implementations by Witten and Frank
• Pattern classification, 2nd Edition, by Duda, Hart, and Stork.
• Principles of data mining by Hand, Mannila, and Smyth.
• The elements of statistical learning by Hastie, Tibshirani, and Friedman.
• Discriminant analysis and statistical pattern recognition by McLachlan.
• Neural networks for pattern recognition by Bishop.
• Introduction to artificial neural systems by Zurada.