Topics in Artificial Intelligence (Learning Theory)

✍ Scribed by Kakade S. Tewari A.

Tongue: English
Leaves: 68
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Toyota Technological Institute at Chicago.
The lecture notes for the course "Topics in Artificial Intelligence", spring 2008.
Sham Kakade and Ambuj Tewari.

Contents:
Mistake Bound Model, Halving Algorithm, Linear Classifiers
Perceptron and Winnow
Online Convex Programming and Gradient Descent
Exponentiated Gradient Descent and Applications of OCP
Game Playing, Boosting
AdaBoost
Probabilistic Setup and Empirical Risk Minimization
Concentration, ERM, and Compression Bounds
Rademacher Averages
Massart’s Finite Class Lemma and Growth Function
VC Dimension and Sauer’s Lemma
VC Dimension of Multilayer Neural Networks, Range Queries
Online to Batch Conversions
Exponentiated Stochastic Gradient Descent for L1 Constrained Problems
Covering Numbers
Dudley’s Theorem, Fat Shattering Dimension, Packing Numbers
Fat Shattering Dimension and Covering Numbers
Rademacher Composition and Linear Prediction

Note: The single pdf file is glued from the multiple pdf files of individual lectures, so the printed pages do not match. On the other hand, there are added bookmarks, allowing to find the topic of interest more easily than via browing the pdf files of individual lectures.

✦ Subjects

Информатика и вычислительная техника;Искусственный интеллект

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