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| Week 1 | Introduction: Machine learning, pattern classification, data mining, information retrieval |
1
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| Week 2 | Introduction: Areas, Brief History, Paradigms |
1
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| Week 3 | Instance-based learning, k-NN |
2
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| Week 4 | Naive Bayes |
8.1, 8.2
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| Week 5 | Non-metric methods: CART, ID3, C4.5 |
8
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| Week 6 | Bayesian learning: Bayesian networks |
6.11, 6.12
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| Week 7 | Evaluation, Experimental Design, Hypothesis Testing |
5.1-5.7
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| Week 8 | Decision Trees: ID3, Decision Stumps |
3.1-3.6
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| Week 9 | Decision Trees: C4.5, Midterm Exam |
3.7-3.8
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| Week 10 | Rule Learning: AQ, OneR |
10.1-10.3
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| Week 11 | Rule Learning: C4.5rules |
5.4
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| Week 12 | Neural Networks, Perceptron |
4.1-4.4
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| Week 13 | Neural Networks, Back-propagation |
4.5-4.7
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| Week 14 | Genetic Algorithms, Genetic Programming |
9.1-9.5
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| Week 15 | Reinforcement Learning, Q-learning, Temporal difference learning |
13.1-13.7
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| 12/7 (out), 12/18 (in) | Final Exam |
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