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Lecture Notes

(Last updated 22 Oct 2002)

• Lecture 0 (Wednesday, August 20, 2003): A Brief Survey of Machine Learning
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• Lecture 1 (Friday, August 22, 200e): Concept Learning and the Version Space Algorithm
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• Lecture 2 (Monday, August 25, 2003): Inductive Bias and PAC Learning
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• Lecture 3 (Wednesday, August 27, 2003): PAC Learning, VC Dimension, and Mistake Bounds
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• Lecture 4 (Friday, August 29, 2003): Decision Trees
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• Lecture 5 (Wednesday, September 03, 2003): Decision Trees, Occam's Razor, and Overfitting
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• Lecture 6 (Friday, September 05, 2003): Perceptrons and Winnow
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• Lecture 7 (Monday, September 08, 2003): Multi-Layer Perceptrons and Backpropagation of Error
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• Lecture 8 (Wednesday, September 10, 2003): Multi-Layer Perceptrons and Backpropagation of Error
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• Lecture 9 (Friday, September 12, 2003): Bayes's Theorem, MAP, and Maximum Likelihood Hypotheses
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• Lecture 10 (Monday, September 15, 2003): Bayesian Classifiers: MDL, BOC, and Gibbs
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• Lecture 11 (Wednesday, September 17, 2003): Simple (Naive) Bayes and Probabilistic Learning over Text
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• Lecture 12 (Tuesday, Septmber 08, 2002): Introduction to Bayesian Networks
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• Lecture 13 (Thursday, October 10, 2002): Learning Bayesian Networks from Data
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• Lecture 14 (Tuesday, October 15, 2002): Midterm Review
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• Lecture 15 (Tuesday, October 22, 2002): Expectation-Maximization (EM), Unsupervised Learning, and Clustering
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• Lecture 16 (Thursday, October 24, 2002): Policy Learning and Markov Decision Processes
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• Lecture 17 (Tuesday, October 29, 2002): Introduction to Reinforcement Learning: Q Learning
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• Lecture 18 (Thursday, October 31, 2002): More Reinforcement Learning: Temporal Differences
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• Lecture 19 (Tuesday, November 05, 2002): Neural Computation
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• Lecture 20 (Thursday, November 07, 2002): Combining Classifiers: Weighted Majority, Bagging, and Stacking
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• Lecture 21 (Tuesday, November 12, 2002): Combning Classifiers: Boosting the Margin and Mixtures of Experts
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• Lecture 22 (Thursday, November 14, 2002): Introduction to Genetic Algorithms
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• Lecture 23 (Tuesday, November 19, 2002): Instance-Based Learning k-Nearest Neighbor and Radial Basis Functions
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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: 08 Sep 2003 CIS732 TA