- Administrivia .
- Mitchell,
*Machine Learning*, Chapter 1 draft: Introduction - Mitchell,
*Machine Learning*, Chapter 2 draft: Concept Learning - Kearns, Li, Pitt, Valiant: "Recent results on boolean concept learning".
- Littlestone: "Learning quickly...".
- Mitchell,
*Machine Learning*, Chapter 3 draft: Decision Tree Learning - Murphy, P.M. and Pazzani, M.J. (1994) "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction", Journal of AI Research, Volume 1, pages 257-275.
- Rivest's lecture notes on Bayesian inference.
- Selection from Minsky and Papert,
*Perceptrons*. - Rumelhart, Hinton, and Williams "Learning internal representations by error propagation" Chapter 8 of
*Parallel Distributed Processing*, Rumelhart and McClelland. - Mitchell, et. al., "Experience with a Learning Personal Assistant", CACM, July, 1994.
- Lang, Waibel, and Hinton, "A time-delay neural network architecture for
isolated word recognition"
*Neural Networks*, (3), pp. 33-43, 1990. - Mitchell,
*Machine Learning*, Chapter 8 draft: Genetic Algorithms - Mitchell,
*Machine Learning*, Chapter 6 draft: Analytical Learning - Mitchell and Thrun,
"Explanation-Based Learning: A Comparison of Symbolic and Neural Network
Approaches",
*Tenth Int. Conf. on Machine Learning*, June, 1993. - Watkins and Dayan, "Q-learning".
- Rivest and Schapire, "Inference of Finite Automata using Homing Sequences", Inf.&Comp. 103, 1993.

- 8/30 - Introduction (plus some extra slides we didn't cover).
- 9/1 - Concept Learning and Version Spaces.
- 9/6 and 9/8 - Mistake bound and PAC models of learning.
- 9/13 - Decision Tree Learning I.
- 9/15 - Decision Tree Learning II: overfitting, pruning, etc..
- 9/20 - Occam's Razor I: PAC model view .
- 9/22 - Occam's Razor II: Bayesian view and decision forest experiment.
- 9/27 - Perceptrons and linear functions.
- 9/29 - Neural network backpropagation
- 10/4 - Case study: Decision tree learning for calendar scheduling ( slides, or reading).
- 10/6 - Dealing with large feature sets: Weighted-Majority based methods
- 10/11 - More on Backpropagation
- 10/13
**Midterm**Open book, open notes - 10/18 Recurrent neural nets . Notes on probabilistic inequalities .
- 10/20 VC-dimension.
- 10/25 Genetic algorithms .
- 10/27 Analytical learning. .
- 11/1 Combining Inductive and Analytical Learning I.
- 11/3 Combining Inductive and Analytical Learning II.
- 11/8 Student presentations: Learning to recognize faces
- 11/10 Nearest neighbor and case-based reasoning .
- 11/15 Boosting and Learning First Order Hypotheses (FOIL and FOCL) .
- 11/17 Hidden Markov Models .
- 11/22 Reinforcement Learning I .
- 11/29 Mathematics of Q-learning .
- 12/1 Reinforcement Learning II .
- 12/12
**FINAL EXAM**5:30 pm

- Assignment 1: Due Sept 8. Problems 1 and 2 from Chapter 2.
- Assignment 2 . Due Sept 15. Note: in problem 1(b), you may use O( ) notation in your answer. Also, in problem 4, the reference should be to 1(a), not 2(a).
- Assignment 3. Decision tree learning of calendar scheduling preferences. Due October 4. See also /afs/cs/project/theo-1/assignment/README. Click here to see code/results/observations that students have made available.
- Assignment 4. Due October 25. ( solutions )
- Assignment 5. Face recognition. Due November 8. What to hand in. The full materials (training images, Backpropagation source code, etc.) are available here.
- Assignment 6. Reinforcement learning. ( solutions )