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**Data Mining**

# 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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