CS 674: Knowledge Discovery

Course Contents:

This course will explore different machine learning, knowledge discovery and data mining approaches and techniques: Concept Learning, Decision Tree Learning, Clustering and instance based learning, Rule induction and inductive learning, Bayesian networks and causality, Neural networks, Genetic algorithms, Reinforcement learning, Analytical learning.

Books and References:

  1. Heikki Mannila, Padhraic Smyth, David Hand.Principles of Data Mining, MIT Press, 2001.

  2. T Hastie, R Tibshirani, J H Friedman.The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Verlag, 2001.

  3. Jensen, F.An Introduction to Bayesian Networks. UCL Press, London, 1996.

  4. Pearl, J.Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, CA, 1988.

  5. Glymour, C., Cooper, G. (eds.)Computation, Causation & Discovery. AAAI Press/The MIT Press, Menlo Park 1999.

  6. Pearl, J.Causality: Models, Reasoning and Inference. Cambridge Unversity Press, 2000.

  7. Ian H Witten, Frank Eibe.Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 1999.

  8. Jiawei Han, Micheline Kamber.Data Mining: Concepts and Techniques. Morgan-Kaufmann, 2000.