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.
Heikki Mannila, Padhraic Smyth, David Hand.Principles of Data Mining, MIT Press, 2001.
T Hastie, R Tibshirani, J H Friedman.The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Verlag, 2001.
Jensen, F.An Introduction to Bayesian Networks. UCL Press, London, 1996.
Pearl, J.Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, CA, 1988.
Glymour, C., Cooper, G. (eds.)Computation, Causation & Discovery. AAAI Press/The MIT Press, Menlo Park 1999.
Pearl, J.Causality: Models, Reasoning and Inference. Cambridge Unversity Press, 2000.
Ian H Witten, Frank Eibe.Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 1999.
Jiawei Han, Micheline Kamber.Data Mining: Concepts and Techniques. Morgan-Kaufmann, 2000.