Lecture Notes  (Last updated 26 Apr 2007)

• Lecture 00 (Friday, 12 Jan 2007) - A Brief Survey of Machine Learning
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• Lecture 01 (Wednesday, 17 Jan 2007) - Concept Learning and the Version Space Algorithm
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• Lecture 02 (Thursday, 18 Jan 2007) - The Candidate Elimination (Version Space)
  Discussion:Algorithm and Inductive Bias
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• Lecture 03 (Friday, 19 Jan 2007) - Inductive Learning Intro: Version Space Algorithm and Representation Bias
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• Lecture 04 (Wednesday, 24 Jan 2007) - Representation Bias vs. Search Bias and Intro to Decision Trees
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• Lecture 05 (Thursday, 25 Jan 2007) - Inductive Bias (continued)and Intro to Decision Trees
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• Lecture 06 (Friday, 26 Jan 2007) - Decision Trees
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• Lecture 07 (Wednesday, 26 Jan 2007) - Decision Trees, Occam's Razor, and Overfitting
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• Lecture 08 (Friday, 09 Feb 2007, rerecorded) - Decision Tree Induction and Overfitting
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• Lecture 09(Friday, 02 Feb 2007) - Decision Trees (Concluded) More on Overfitting Avoidance
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• Lecture 10(Wednesday, 07 Feb 2007) - Perceptrons and Winnow
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• Lecture 11 (Thursday, 08 Feb 2007)(Re-Recorded) - Artificial Neural Networks (ANNs):More Perceptrons and Winnow
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• Lecture 12 (Friday, 09 Feb 2007) - Multilayer Perceptrons and Intro to Support Vector Machines
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• Lecture 13 (Wednesday, 14 Feb 2007) - Multilayer Perceptrons and Intro to Support Vector Machines
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• Lecture 14 (Thursday, 15 Feb 2007) - Support Vector Machines
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• Lecture 15 (Friday, 16 Feb 2007) - Genetic and Evolutionary Computation 1 of 3: The Simple Genetic Algorithm
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• Lecture 16 (Wednesday, 21 Feb 2007) - Intro to Genetic Algorithms (continued)and Bayesian Preliminaries
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• Lecture 17 (Thursday, 22 Feb 2007) - SVM Continued and Intro to Bayesian Learning: Max a Posteriori and Max Likelihood Estimation
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• Lecture 18 (Friday, 23 Feb 2007) - Bayes's Theorem, MAP,and Maximum Likelihood Hypotheses
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• Lecture 19 (Wednesday, 28 Feb 2007) - MAP and MLE continued, Minimum Description Length (MDL)
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• Lecture 20 (Thursday, 01 Mar 2007) - Bayesian Classifiers: MDL, BOC, and Gibbs
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• Lecture 21 (Friday, 02 Mar 2007) -Introduction to Bayesian Networks
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• Lecture 22 (Wednesday, 07 Mar 2007) -Learning Bayesian Networks
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• Lecture 23 (Thursday, 08 Mar 2007) -Bayesian Networks Midterm Review 1 of 2
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• Lecture 24 (Friday, 09 Mar 2007) -Bayesian Networks: Learning Distributions COLT: PAC and VC Dimension
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• Lecture 25 (Thursday, 15 Mar 2007) -PAC Learning, VC Dimension, and Mistake Bounds
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• Lecture 26 (Friday, 16 Mar 2007) -More Computational Learning Theory and Classification Rule Learning
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• Lecture 27 (Wednesday, 28 Mar 2007) -Combining Classifiers: Weighted Majority, Bagging, and Stacking
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• Lecture 28 (Thursday, 29 Mar 2007) -Combining Classifiers: Weighted Majority, Bagging, Stacking, Mixtures
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• Lecture 29 (Friday, 30 Mar 2007) -Combining Classifiers: Boosting the Margin and Mixtures of Experts
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• Lecture 30 (Wednesday, 04 Apr 2007) -Instance-Based Learning (IBL): k-Nearest Neighbor and Radial Basis Functions
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• Lecture 31 (Thursday, 05 Apr 2007) -Instance-Based Learning (IBL): k-NN, RBFs Intro to Rule Learning
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• Lecture 32 (Friday, 06 Apr 2007) -Inductive Logic Programming (ILP) and Rule Learning and Extraction
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• Lecture 33 (Wednesday, 11 Apr 2007) -Intro to Rule Learning
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• Lecture 34 (Thursday, 12 Apr 2007) -Classification Rule Learning (concluded) and Association Rule Learning
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• Lecture 36 (Wednesday, 18 Apr 2007) -Expectation Maximization (EM), Unsupervised Learning and Clustering
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• Lecture 37 (Thursday, 19 Apr 2007) -Unsupervised Learning: AutoClass, SOM, EM, and Hierarchical Mixtures of Experts
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• Lecture 38 (Friday, 20 Apr 2007) -Learning Bayesian Networks from Data
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• Lecture 39 (Wednesday, 25 Apr 2007) -Policy Learning and Markov Decision Processes
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• Lecture 40 (Thursday, 26 Apr 2007) -Introduction to Reinforcement Learning: Q Learning
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• Lecture 41 (Friday, 27 Apr 2007) -Q Learning and Temporal Difference Learning
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