UID:
almafu_9961535628302883
Format:
1 online resource (X, 200 p.)
ISBN:
9783111288994
Series Statement:
De Gruyter Textbook
Content:
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
Note:
Frontmatter --
,
Preface --
,
Contents --
,
Part I: Introduction and preliminaries --
,
1 Introduction to machine learning --
,
2 Probability review --
,
3 Optimization --
,
Part II: Supervised learning --
,
4 Statistical learning theory --
,
5 Linear models --
,
6 Kernel methods --
,
7 Gaussian processes --
,
8 Deep learning --
,
9 Ensemble methods --
,
Part III: Beyond supervised learning --
,
10 Topics in unsupervised learning --
,
11 Reinforcement learning --
,
Bibliography --
,
Index
,
Issued also in print.
,
In English.
Additional Edition:
ISBN 9783111289816
Additional Edition:
ISBN 9783111288475
Language:
English
