Introduction. Overview of learning (ch. 1), WeH 4623
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NO CLASS
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Concept learning, version spaces (ch. 2), WeH 4623 ===>Assignment 1 (Out)
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Version Spaces, inductive bias (ch. 2). WeH 4623
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Version Spaces (cont), Tutorial in Information Theory.
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Tutorial in Information Theory (cont). ===>Assignment 2 (Out)
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Decision trees, overfitting, Occam's
razor (ch. 3).
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Decision trees, overfitting, Occam's razor (cont.).
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Neural networks (ch. 4) ===>Assignment 3 (Out)
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Neural networks cont.
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NO CLASS ===>Assignment 4 (Out)
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Neural networks cont.
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Neural networks cont. ===>Assignment 5 (Out)
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NO CLASS
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PAC learning, VC dimension, Mistake bounds (ch. 7 through 7.3, 7.4 through 7.4.3, 7.5 through 7.5.3)
lecture
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Mid-term Exam (Finalized date!)
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Statistical Estimation and Testing (ch.
5) ===>Assignment 6 (Out) .
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Bayesian learning: MAP and ML learners (ch.
6)
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Bayes learning examples ===>Assignment 7 (Out)
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MDL (ch.
6) Bayes Optimal Classifier, Gibbs sampling. Naive Bayes and
learning over text (ch
6)
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Naive Bayes (cont), Bayes Nets
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Bayes Nets (cont.), E-M algorithm (ch 6) ===>Assignment 8 (Out)
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Hidden Markov Models (HMM) (ps,pdf)
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HMM (cont.), Examples from Speech
Recognition ===> Assignment 9 (Out)
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Instance based learning, k nearest nbr.,
locally weighted regression,
radial basis functions (ch. 8) 1 per
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Local methods (cont.), Reinforcement learning (ch. 13)
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Reinforcement learning (cont.)
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NO CLASS (Thanksgiving)
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===>Assignment 10 (Out)
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Combining
Learned Classifiers, Weighted
Majority, Bagging,
Boosting (1),
(2)
(ch. 7: Weighted majority)
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Genetic algorithms, genetic programming
(ch. 9)
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Assignment 10 due at 11:59 p.m: NO EXTENSIONS OR LATE DAYS
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Final Exam: 5:30pm - 8:30pm, Location: BH 136A
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