COSC-288: Introduction to Machine Learning

Course Description

This course surveys the major research areas of machine learning, concentrating on inductive learning. Topics include classification, anomaly detection, clustering, and reinforcement learning. Specific methods include rule induction, decision trees, neural networks, instance-based approaches, support vector machines, genetic algorithms, evaluation, and applications. In addition to programming projects and homework, students will complete midterm and final examinations. 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.