| • | Introduction | 1.5 hours |
| • | Version space learning; Computational learning theory | 4.0 hours |
| • | PAC-learning; VC-dimension; On-line learning; Winnow | 4.0 hours |
| • | Perceptrons; Neural Networks; Backpropagation | 6.0 hours |
| • | Genetic algorithms | 3.5 hours |
| • | Bayesian learning | 3.5 hours |
| • | Experimental design | 3.0 hours |
| • | Decision-tree learning | 3.5 hours |
| • | Covering algorithms for learning rule sets | 3.0 hours |
| • | Minimum description length | 3.5 hours |
| • | Clustering algorithms | 3.0 hours |
| • | Reinforcement learning | 3.5 hours |
| • | Markov decision processes | 3.0 hours |
| Total | 45 hours | |