Machine Learning and Data Mining

As a result of successfully completing this course students will be able to set up well-defined learning problems, apply effective algorithms to such such problems and use the relevant theory to interpret and evaluate the results. By the end of the subject, students should be able to: • set up a well-defined learning problem for a given task • select and define a representation for data to be used as input to a machine learning algorithm • select and define a representation for the model to be output by a machine learning algorithm • compare algorithms according to the properties of their inputs and outputs Topic Material Week 1 Course Introduction, Introduction to Machine Learning, Fundamentals of Concept Learning Week 2 Fundamentals of Concept Learning (Revised), Concept Learning Exercises, Decision Tree Learning Week 3 Rule Learning, Note on Support Week 4 Machine Learning for Numeric Prediction, Revised Notes Week 5 Instance Based Learning, Genetic Algorithms Week 6 MidTerm Review, Bias Week 7 Evaluating hypotheses, ML_part_V.pdf Week 8 Bayesian Learning Week 9 Guest Lecture: Reinforcement Learning Week 10 Learning Theory, Supplementary, Supplementary (corrected) Week 11 Ensembles, etc., NFL Week 12 Unsupervised Learning Week 13 Learning and Logic, Relational learning on text Week 14 Final exam 2003, Some topics

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