• Title: Statistical Learning and Sequential Prediction
• Author(s) Alexander Rakhlin, Karthik Sridharan
• Publisher: Massachusetts Institute of Technology (2014)
• Hardcover/Paperback: N/A
• eBook: PDF (261 pages)
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
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Book Description

This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction. Until recently, these two subjects have been treated separately within the learning community. It follows a unified approach to analyzing learning in both scenarios. To make this happen, it brings 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 authors hope to convey the excitement.

The authors shall try to make the course as self-contained as possible, and pointers to additional readings will be provided whenever necessary. The target audience is graduate students with a solid background in probability and linear algebra.

Why should one care about machine learning? Many tasks that we would like computers to perform cannot be hard-coded. The programs have to adapt. The goal then is to encode, for a particular application, as much of the domain-specific knowledge as needed, and leave enough flexibility for the system to improve upon observing data.

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• Machine Learning
• Statistics, Mathematical Statistics
• Probability and Stochastic Processes
• Data Analysis and Data Mining
• Artificial Intelligence

• Statistical Learning and Sequential Prediction (Alexander Rakhlin, et al)
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