Lecture 11: Learning

For this lecture we will use the material from 
http://www.myreaders.info/html/artificial_intelligence.html
chapter 6 Learning Systems (http://www.myreaders.info/06_Learning_Systems.pdf)

Sections:

1. What is learning (slides 3-9)
2. Rote learning (10)
3. Learning from example - Induction (11-38)
4. Explanation based learning (39-43)
5. Discovery (44-52)
6. Clustering (53-62)
7. Analogy (63)
8. Neural net and Genetic learning (64-67)
9. Reinforcement learning (68-80)

Algorithms described in the various sections:

3: Version space search algorithm (slides 17-18, example 19-23)
3: ID3 Algorithm (25-30, example 31-37)
4: EBL Algorithm (41, example 42-43)
5: AM system (45-46)
5: Bacon system (47)
5: BACON.1 (48, example 48-50)
5: BACON.3 (51, example 52)
6: K-Means Clustering (57, example 58-62)
8: Genetic Algorithms (66-67)
9: Reinforcement Learning Problem (69-70)
9: Markov Decision Process (Markov Chain) (71-73, 76-80, example 74-75)