Kooperativer Bibliotheksverbund

UID:

Umfang: 1 Online-Ressource (IX, 199 Seiten) : , Illustrationen, Diagramme.

ISBN: 978-3-11-128899-4 , 978-3-11-128981-6

Serie: De Gruyter Textbook

Inhalt: 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

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-11-128847-5

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Maschinelles Lernen ; Angewandte Mathematik

DOI: 10.1515/9783111288994

URL: Volltext  (URL des Erstveröffentlichers)

URL: lizenzpflichtig

URL: lizenzpflichtig

URL: Cover

UID:

Umfang: IX, 199 Seiten , Illustrationen , 24 cm x 17 cm, 356 g

Ausgabe: 1. Auflage

ISBN: 9783111288475 , 3111288471

Serie: De Gruyter Textbook

Anmerkung: Erscheint auch als Online-Ausgabe 9783111289816 (ISBN)

Sprache: Englisch

URL: https://www.degruyter.com/isbn/9783111288475

URL: https://d-nb.info/1316569888/04

UID:

Umfang: IX, 199 Seiten , Illustrationen, Diagramme , 24 cm x 17 cm, 356 g

ISBN: 9783111288475 , 3111288471

Serie: De Gruyter Graduate

Weitere Ausg.: Erscheint auch als Online-Ausgabe, PDF ISBN 978-3-11-128899-4

Weitere Ausg.: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-3-11-128981-6

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Maschinelles Lernen ; Angewandte Mathematik

URL: https://www.degruyter.com/isbn/9783111288475

URL: Inhaltsverzeichnis

URL: Inhaltsverzeichnis

UID:

Umfang: 1 online resource (X, 200 p.)

ISBN: 9783111288994

Serie: De Gruyter Textbook

Inhalt: 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.

Anmerkung: 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.

Weitere Ausg.: ISBN 9783111289816

Weitere Ausg.: ISBN 9783111288475

Sprache: Englisch

DOI: 10.1515/9783111288994

URL: https://doi.org/10.1515/9783111288994

URL: https://www.degruyter.com/isbn/9783111288994

URL: https://doi.org/10.1515/9783111288994

URL: https://www.degruyter.com/isbn/9783111288994

UID:

Umfang: 1 Online-Ressource (IX, 199 Seiten)

Ausgabe: Reproduktion Issued also in print

ISBN: 9783111288994 , 9783111289816

Serie: De Gruyter textbook

Inhalt: 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

Anmerkung: 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

Weitere Ausg.: ISBN 9783111288475

Weitere Ausg.: Erscheint auch als print ISBN 9783111288475

Sprache: Englisch

Schlagwort(e): Überwachtes Lernen ; Deep Learning ; Mathematik ; Optimierung ; Lineare Regression ; Hilbert-Raum mit reproduzierendem Kern ; Gauß-Prozess

DOI: 10.1515/9783111288994

URL: lizenzpflichtig

URL: lizenzpflichtig

URL: Cover