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. Students must take midterm and final exams, and complete five programming projects using a language of their choosing. COSC 173 is a prerequisite.

Primary Text:

• 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.