Tue, Jan 21 |
Organizational issues. Course outline.
Probabilistic formulations of prediction problems.
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01-notes.pdf.
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Thu, Jan 23 |
Plug-in estimators. Linear threshold functions. Perceptron algorithm.
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02-notes.pdf.
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Tue, Jan 28 |
Minimax risk. Bounds for linear threshold functions.
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03-notes.pdf.
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Thu, Jan 30 |
Concentration inequalities.
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04-notes.pdf.
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Tue, Feb 4 |
Concentration inequalities.
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05-notes.pdf.
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Thu, Feb 6 |
Concentration inequalities.
Uniform laws of large numbers.
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06-notes.pdf.
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Tue, Feb 11 |
Uniform laws of large numbers.
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07-notes.pdf.
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Thu, Feb 13 |
Vapnik-Chervonenkis dimension.
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08-notes.pdf.
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Tue, Feb 18 |
Online learning (Wouter Koolen presenting):
Mix loss. Dot loss.
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09-notes.pdf.
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Thu, Feb 20 |
Minimax with dot-loss. Follow the perturbed leader.
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10-notes.pdf.
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Tue, Feb 25 |
Follow the perturbed leader. Adaptive regret and tracking.
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11-notes.pdf.
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Thu, Feb 27 |
Normalized maximum likelihood. Universal portfolios.
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12-notes.pdf.
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Tue, Mar 4 |
Universal portfolios, Context tree weighting.
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13-notes.pdf.
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Thu, Mar 6 |
Reductions: mixability, gradient trick, specialists.
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14-notes.pdf.
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Tue, Mar 11 |
Online convex optimization.
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15-notes.pdf.
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Thu, Mar 13 |
Online convex optimization: Regularization.
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16-notes.pdf.
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Tue, Mar 18 |
Online convex optimization: Regret bounds.
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17-notes.pdf.
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Thu, Mar 20 |
Optimal regret.
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18-notes.pdf.
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Tue, Mar 25 | Spring |
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Thu, Mar 27 | Break |
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Tue, Apr 1 |
Kernel methods.
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19-notes.pdf.
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Thu, Apr 3 |
Kernels, RKHSs, Mercer's Theorem.
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20-notes.pdf.
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Tue, Apr 8 |
Hard margin SVMs, optimization.
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21-notes.pdf.
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Thu, Apr 10 |
Soft margin SVMs, representer theorem.
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22-notes.pdf.
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Tue, Apr 15 |
Risk/regret bounds for SVMs.
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23-notes.pdf.
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Thu, Apr 17 |
Kernel regression. Convex losses for classification.
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24-notes.pdf.
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Tue, Apr 22 |
AdaBoost.
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25-notes.pdf.
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Thu, Apr 24 |
AdaBoost as I-projection.
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26-notes.pdf.
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Tue, Apr 29 |
Convergence of AdaBoost.
Model selection, complexity regularization,
consistency of AdaBoost.
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27-notes.pdf.
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Thu, May 1 |
Final project presentations.
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