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Lecture Notes

(Last updated 11 Feb 2002)

• Lecture 0 (Tuesday, August 21, 2001): A Brief Survey of Machine Learning
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• Lecture 1 (Thursday, August 23, 2001): Concept Learning and the Version Space Algorithm
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• Lecture 2 (Tuesday, August 28, 2001): Inductive Bias and PAC Learning
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• Lecture 3 (Thursday, August 30, 2001): PAC Learning, VC Dimension, and Mistake Bounds
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• Lecture 4 (Tuesday, September 04, 2001): Decision Trees
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• Lecture 5 (Thursday, September 06, 2001): Decision Trees, Occam's Razor, and Overfitting
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• Lecture 6 (Tuesday, September 11, 2001): Perceptrons and Winnow
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• Lecture 7 (Thursday, September 13, 2001): Multi-Layer Perceptrons and Backpropagation of Error
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• Lecture 9 (Thursday, September 20, 2001): Bayes's Theorem, MAP, and Maximum Likelihood Hypotheses
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• Lecture 10 (Tuesday, September 25, 2001): Bayesian Classifiers: MDL, BOC, and Gibbs
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• Lecture 11 (Thursday, September 27, 2001): Simple (Naive) Bayes and Probabilistic Learning over Text
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• Lecture 12 (Tuesday, October 2, 2001): Introduction to Bayesian Networks
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• Lecture 13 (Thursday, October 4, 2001): Learning Bayesian Networks from Data
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• Lecture 14 (Tuesday, October 9, 2001): Midterm Review
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• Lecture 15 (Tuesday, October 16, 2001): Expectation-Maximization (EM), Unsupervised Learning, and Clustering
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• Lecture 16 (Thursday, October 18, 2001): Time Series and Stochastic Processes
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• Lecture 17 (Tuesday, October 23, 2001): Policy Learning and Markov Decision Processes
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• Lecture 18 (Thursday, October 25, 2001): Introduction to Reinforcement Learning: Q Learning
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• Lecture 19 (Tuesday, October 30, 2001): More Reinforcement Learning: Temporal Differences
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• Lecture 20 (Thursday, November 1, 2001): Neural Computation
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• Lecture 21 (Tuesday, November 6, 2001): Combining Classifiers: Weighted Majority, Bagging, and Stacking
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• Lecture 22 (Thursday, November 8, 2001): Combining Classifiers: Boosting the Margin and Mixtures of Experts
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• Lecture 23 (Tuesday, November 13, 2001): Introduction to Genetic Algorithms
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• Lecture 24 (Thursday, November 15, 2001): Introduction to Genetic Programming
• Lecture 25 (Tuesday, November 20, 2001): Instance-Based Learning (IBL): k-Nearest Neighbor and Radial Basis Functions
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• Lecture 26 (Tuesday, November 27, 2001): Rule Learning and Extraction
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• Lecture 27 (Thursday, November 29, 2001): Inductive Logic Programming (ILP)
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• Lecture 28 (Tuesday, December 4, 2001): Knowledge Discovery in Databases (KDD) and Data Mining
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• Lecture 29 (Thursday, December 6, 2001): Conclusions and Final Review
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Page created: 03 Sep 2001 Last updated: 02 Jun 2002 William H. Hsu