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8-Jan
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Introduction
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10-Jan
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Concept Learning
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15-Jan
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Concept Learning - Decision Trees
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17-Jan
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Decision Trees - Wenke Lee
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22-Jan
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Evaluating Hypotheses
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24-Jan
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Neural Networks
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29-Jan
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Bayesian Probabilities and the Joint PDF
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31-Jan
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Densities and Likelihoods
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5-Feb
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Bayes Classifiers, the Sigmoid, and Naïve Bayes
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7-Feb
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Instance-based Learning (Regression)
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12-Feb
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Janet Kolodner Guest Lecture
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14-Feb
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Instance-based Learning (Classification)
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19-Feb
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Mixture Densities and RBF nets
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21-Feb
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Midterm Review
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26-Feb
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Midterm
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28-Feb
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Neural Networks
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5-Mar
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SPRING BREAK
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7-Mar
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SPRING BREAK
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12-Mar
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Neural Networks
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14-Mar
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Support-Vector Machines
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19-Mar
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SVM (Continued)
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21-Mar
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SVM (Continued)
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26-Mar
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Boosting
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28-Mar
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Boosting
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2-Apr
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Bayesian Belief Networks - Intro
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4-Apr
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Bayesian Belief Networks - Learning
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9-Apr
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Jim Rehg Guest Lecture
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11-Apr
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Genetic Algorithms - Simulated Annealing
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16-Apr
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Reinforcement Learning
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18-Apr
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Project Presentations
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23-Apr
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Project Presentations
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25-Apr
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Review
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3-May
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Final Exam
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