**by Alexander Rakhlin, Karthik Sridharan**

DescriptionTable of ContentsDetailsHashtagsReport an issue ### Book Description

This free book will focus on theoretical aspects of Statistical Learning and Sequential Prediction. Until recently, these two subjects have been treated separately within the learning community. The course will follow a unified approach to analyzing learning in both scenarios. To make this happen, we shall bring together ideas from probability and statistics, game theory, algorithms, and optimization. It is this blend of ideas that makes the subject interesting for us, and we hope to convey the excitement. We shall try to make the course as self-contained as possible, and pointers to additional readings will be provided whenever necessary. Our target audience is graduate students with a solid background in probability and linear algebra. ### Table of Contents

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This open book is licensed under a Creative Commons License (CC BY). You can download Statistical Learning and Sequential Prediction ebook for free in PDF format (3.4 MB).

Part I

Introduction

Part II

Theory

Minimax Formulation of Learning Problems

Learnability, Oracle Inequalities, Model Selection, and the Bias-Variance Trade-off

Stochastic processes, Empirical processes, Martingales, Tree Processes

Example: Learning Thresholds

Maximal Inequalities

Example: Linear Classes

Statistical Learning: Classification

Statistical Learning: Real-Valued Functions

Sequential Prediction: Classification

Sequential Prediction: Real-Valued Functions

Examples: Complexity of Linear and Kernel Classes, Neural Networks

Large Margin Theory for Classification

Regression with Square Loss: From Regret to Nonparametric Estimation

Part III

Algorithms

Algorithms for Sequential Prediction: Finite Classes

Algorithms for Sequential Prediction: Binary Classification with Infinite Classes

Algorithms for Online Convex Optimization

Example: Binary Sequence Prediction and the Mind Reading Machine

Algorithmic Framework for Sequential Prediction

Algorithms Based on Random Playout, and Follow the Perturbed Leader

Algorithms for Fixed Design

Adaptive Algorithms

Part IV

Extensions

The Minimax Theorem

Two Proofs of Blackwell's Approachability Theorem

From Sequential to Statistical Learning: Relationship Between Values and Online-to-Batch

Sequential Prediction: Competing With Strategies

Localized Analysis and Fast Rates. Local Rademacher Complexities

Appendix

Publisher

MIT Press

Published

2014

Pages

261

Edition

1

Language

English

PDF Size

3.4 MB

License

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