| Week | Project | Topic | PDF Slides |
| 1 | | Introduction - Machine Learning | |
| | Introduction - Weka | | |
| 2 | | Data Preprocessing | |
| Project 1 presentations | | ||
| 3 | | Decision Trees (I) | |
| | Decision Trees (II) | ||
| 4 | Project 2 presentations | | |
| | Neural Networks (I) | ||
| 5 | | Neural Networks (II) | |
| | Neural Networks (III) | ||
| 6 | Project 3 presentations | | |
| | Evaluating Hypothesis (I) | ||
| 7 | | Evaluating Hypothesis (II) | |
| Project 4 presentations | | ||
| 8 | | Bayesian Learning (I) | |
| | Bayesian Learning (II) | ||
| 9 | | Bayesian Learning (III) - K2 algorithm | |
| Project 5 presentations | | ||
| 10 | | Instance-based Learning | |
| Project 6 presentations | | ||
| 11 | | Genetic Algorithms (I) | |
| | Genetic Algorithms (II) | ||
| 12 | Project 7 presentations | | |
| | Rule Learning (I) | ||
| 13 | | Rule Learning (II) | |
| | Reinforcement Learning |
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Machine Learning Slides
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