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7-Jan
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Introduction
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9-Jan
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Concept Learning
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14-Jan
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Decision Trees
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16-Jan
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Neural Networks
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21-Jan
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Neural Networks: Cont'd
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23-Jan
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Evaluating Hypotheses
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28-Jan
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Bayesian Learning
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30-Jan
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Maximum Likelihood
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4-Feb
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Naïve Bayes, MDL
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6-Feb
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Binary Classification and Logistic Regression
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11-Feb
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Optimal Bayes & Gibbs Algorithm
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13-Feb
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Clustering/Mixture Densities/EM
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18-Feb
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Graphical Models: Inference & Simulation
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20-Feb
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Belief propagation (chains, MRF, factor graphs)
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25-Feb
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Midterm Review
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27-Feb
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Midterm
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4-Mar
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SPRING BREAK
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6-Mar
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SPRING BREAK
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11-Mar
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Junction Trees and HMM's
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13-Mar
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Learning via EM, Markov chain Monte Carlo
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18-Mar
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Computational Learning Theory
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20-Mar
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Support-Vector Machines
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25-Mar
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Support-Vector Machines (cont'd)
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27-Mar
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Boosting
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1-Apr
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Tucker Balch: Intro to reinforcement Learning
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3-Apr
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Charles Isbell: Independent Component Analysis Talk
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8-Apr
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Sven Koenig: Markov Decision Processes
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10-Apr
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Markov Decision Processes & Reinforcement Learning
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15-Apr
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Instance-based Learning
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17-Apr
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Project Presentations
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22-Apr
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Project Presentations
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24-Apr
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Review
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30-Apr
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Final Exam
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