Lecture Notes  (Last updated 19 Jan 2011)

• Lecture 00 (Friday, 16 Jan 2009) - Machine Learning / Advanced Topics in AI
  Course Organization and Survey
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• Lecture 01 (Mon, 19 Jan 2009) - Introduction to Machine Learning and the Version Space Formalism
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• Lecture 02 (Wed, 21 Jan 2009) - Representation Bias vs. Search Bias and Intro to Decision Trees
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• Lecture 04 (Mon, 26 Jan 2009) - Decision Trees
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• Lecture 05 (Wed, 28 Jan 2009) - Decision Trees, Occam's Razor, and Overfitting
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• Lecture 06 (Fri, 30 Jan 2009) - Bayes's Theorem, MAP,and Maximum Likelihood Hypotheses
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• Lecture 07 (Mon, 02 Feb 2009) - Simple (Naive) Bayes and Probabilistic Learning over Text
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• Lecture 08 (Wed, 04 Feb 2009) - Simple (Naive) Bayes and Probabilistic Learning over Text
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• Lecture 09 (Fri, 06 Feb 2009) - Perceptrons and Winnow
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• Lecture 11 (Wed, 11 Feb 2009) - Artificial Neural Networks: Backprop (continued) and Intro to SVM
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• Lecture 12 (Fri, 13 Feb 2009) - Support Vector Machines
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• Lecture 13 (Mon, 16 Feb 2009) - Support Vector Machines
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• Lecture 14 (Wed, 18 Feb 2009) - Instance-Based Learning (IBL): k-Nearest Neighbor and Radial Basis Functions
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• Lecture 15 (Fri, 20 Feb 2009) - Introduction to Genetic Algorithms
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• Lecture 16 (Mon, 23 Feb 2009) - Regression and Prediction
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• Lecture 17 (Wed, 25 Feb 2009) - Methods for Statistical Evaluation of Hypotheses
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• Lecture 18 (Fri, 27 Feb 2009) - Combining Classifiers: Weighted Majority, Bagging, and Stacking
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• Lecture 19 (Mon, 02 Mar 2009) - Combining Classifiers: Boosting the Margin and Mixtures of Experts
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• Lecture 20 (Wed, 04 Mar 2009) - Expectation Maximization (EM), Unsupervised Learning and Clustering
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• Lecture 21 (Fri, 06 Mar 2009) - Partitioning-Based Clustering and Expectation Maximization (EM)
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• Lecture 23 (Wed, 11 Mar 2009) - Density- and grid-based clustering
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• Lecture 24 (Fri, 13 Mar 2009) - Model-Based Clustering: Expectation-Maximization
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• Lecture 27 (Fri, 20 Mar 2009) - Time Series Data and Data Streams
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