| 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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