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


 Title: Statistical Foundations of Machine Learning: The Handbook
 Author(s): Gianluca Bontempi
 Publisher: Universite Libre de Bruxelles (February 15, 2022)
 Hardcover/Paperback: N/A
 eBook: PDF (364 pages)
 Language: English
 ISBN10: N/A
 ISBN13: N/A
 Share This:
Book Description
We are in the era of big data. There are essentially two reasons why people gather increasing volumes of data: first, they think some valuable assets are implicitly coded within them, and second computer technology enables effective data storage at reduced costs.
The procedure for finding useful patterns in data is known by different names in different communities but more and more, the set of computational techniques and tools to support the modelling of large amount of data is grouped under the label of machine learning.
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. In particular, we focus on supervised learning problems, where the goal is to model the relation between a set of input variables, and one or more output variables, which are considered to be dependent on the inputs in some manner.
Since the handbook deals with artificial learning methods, we do not take into consideration any argument of biological or cognitive plausibility of the learning methods we present. Learning is postulated here as a problem of statistical estimation of the dependencies between variables on the basis of data.
This book aims to find a good balance between theory and practice by situating most of the theoretical notions in a real context with the help of practical examples and real datasets. All the examples are implemented in the statistical programming language R. This practical connotation is particularly important since machine learning techniques are nowadays more and more embedded in plenty of technological domains, like bioinformatics, robotics, intelligent control, speech and image recognition, multimedia, web and data mining, computational finance and business intelligence.
About the AuthorsN/A
 Statistics, Mathematical Statistics, and SAS Programming
 Machine Learning
 Big Data
 Data Analysis and Data Mining
 The R Programming Language
 Statistical Foundations of Machine Learning: The Handbook (Gianluca Bontempi)
 The Mirror Site (1)  PDF
 The Mirror Site (2)  PDF

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 Learning and Sequential Prediction
This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.
:






















