Introduction. Overview of learning (ch. 1)
lecture slides: 1 per page or
4 per page |
Concept learning, version spaces (ch. 2)
lecture slides: 1 per page or
4 per page |
| Version Spaces, inductive bias (ch. 2) |
PAC learning (ch.7 through 7.3)
lecture slides: 1 per page or
4 per page |
| no class |
PAC learning, VC dimension, Mistake bounds
(ch. 7.4 through 7.4.3, 7.5 through 7.5.3) |
Tutorial in Information Theory
(joint presentation to 15681/15781, usual classroom)
lecture slides: 1 per page
or 4 per page |
Decision trees, overfitting, Occam's razor (ch. 3)
lecture slides: 1 per page or
4 per page |
| Decision trees, overfitting, Occam's razor (cont.) |
Statistical Estimation and Testing (ch. 5).
lecture slides: 1 per page or
4 per page |
VC-dimension problems. Begin Neural networks (ch. 4)
lecture slides: 1 per page or
4 per page |
| Neural networks cont. (ch. 4) |
| Neural networks cont. (ch. 4) |
| Tutorial on Linearly Separable Functions |
| Review for midterm |
| MIDTERM EXAM
grade distribution |
Bayesian learning: MAP and ML learners (ch. 6)
lecture slides: 1 per page or
4 per page |
| Bayes learning examples; MDL (ch. 6) |
| no class |
| midter review |
Bayes Optimal Classifier, Gibbs sampling,
Naive Bayes and learning over text (ch 6) |
| Naive Bayes and Bayes nets (ch 6) |
| Expectation Maximization (EM) |
| Hidden Markov Models (HMM); Examples from speech recognition (ps,pdf) |
| HMM (cont.); Speech Recognition |
| Thanksgiving Break - No Class |
Combining Learned Classifiers,
Weighted Majority, Bagging,
Boosting (1), (2)
(ch. 7: Weighted majority) |
Genetic algorithms, genetic programming (ch. 9)
lecture slides: 1 per page or
4 per page |
Instance based learning, k nearest nbr., locally weighted regression,
Radial basis functions (ch. 8) 1 per
page or 4 per page |
Reinforcement learning (ch. 13)
lecture slides: 1 per page or
4 per page |
| Review for final |
| FINAL EXAM |
