FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 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
 ISBN10: N/A
 ISBN13: N/A
 Share This:
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 selfcontained 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 hardcoded. The programs have to adapt. The goal then is to encode, for a particular application, as much of the domainspecific knowledge as needed, and leave enough flexibility for the system to improve upon observing data.
About the Author(s) N/A
 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)
 The Mirror Site (1)  PDF
 The Book Homepage

An Introduction to Statistical Learning (Gareth James, et al)
It provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

The Elements of Statistical Learning: Data Mining, Inference, etc.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.

Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudocode for the most important algorithms.

Statistical Foundations of Machine Learning: The Handbook
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.
:






















