Maschinelles Lernen

Ethem Alpaydin

Published by De Gruyter Oldenbourg 2022

Maschinelles Lernen

Machine Learning

Ethem Alpaydin

In the series De Gruyter Studium

https://doi.org/10.1515/9783110740196

Overview
Contents

About this book

Machine learning means artificially generating knowledge from experience. This book discusses methods from the fields of statistics and pattern recognition, and combines various approaches to find efficient solutions. This edition contains a new chapter about deep learning and provides additional information about multilayer perceptrons and reinforcement learning. There is also a new section on generative adversarial networks.

Das Standardwerk über maschinelles Lernen
Die neue Auflage mit neuen Sektionen über Deep Learning, mehrlagige Perzeptoren und Generative Adversarial Networks
Mit neuen Appendizen über die Lineare Algebra und Optimisierung.

Author / Editor information

Ethem Alpaydin, Özyegin University of Istanbul, Istanbul, Turkey.

Topics

Frontmatter Publicly Available Download PDF	I
Inhaltsverzeichnis Publicly Available Download PDF	V
Vorwort Publicly Available Download PDF	XV
Notationen Publicly Available Download PDF	XIX
1 Einführung Requires Authentication Unlicensed Licensed Download PDF	1
2 Überwachtes Lernen Requires Authentication Unlicensed Licensed Download PDF	25
3 Bayessche Entscheidungstheorie Requires Authentication Unlicensed Licensed Download PDF	53
4 Parametrische Methoden Requires Authentication Unlicensed Licensed Download PDF	71
5 Multivariate Methoden Requires Authentication Unlicensed Licensed Download PDF	101
6 Dimensionalitätsreduktion Requires Authentication Unlicensed Licensed Download PDF	125
7 Clusteranalyse Requires Authentication Unlicensed Licensed Download PDF	173
8 Nichtparametrische Methoden Requires Authentication Unlicensed Licensed Download PDF	199
9 Entscheidungsbäume Requires Authentication Unlicensed Licensed Download PDF	227
10 Lineare Diskriminanz Requires Authentication Unlicensed Licensed Download PDF	255
11 Mehrlagige Perzeptronen Requires Authentication Unlicensed Licensed Download PDF	285
12 Tiefes Lernen Requires Authentication Unlicensed Licensed Download PDF	327
13 Lokale Modelle Requires Authentication Unlicensed Licensed Download PDF	375
14 Kernel-Maschinen Requires Authentication Unlicensed Licensed Download PDF	409
15 Graphenmodelle Requires Authentication Unlicensed Licensed Download PDF	447
16 Hidden-Markov-Modelle Requires Authentication Unlicensed Licensed Download PDF	479
17 Bayessche Schätzung Requires Authentication Unlicensed Licensed Download PDF	507
18 Kombination mehrerer Lerner Requires Authentication Unlicensed Licensed Download PDF	551
19 Bestärkendes Lernen Requires Authentication Unlicensed Licensed Download PDF	581
20 Experimente mit maschinellem Lernen Requires Authentication Unlicensed Licensed Download PDF	617
A Wahrscheinlichkeit Requires Authentication Unlicensed Licensed Download PDF	663
B Lineare Algebra Requires Authentication Unlicensed Licensed Download PDF	675
C Optimierung Requires Authentication Unlicensed Licensed Download PDF	685
Index Requires Authentication Unlicensed Licensed Download PDF	693

(Deutsch)

Language: German
Publisher: De Gruyter Oldenbourg
Edition: 3., aktualisierte und erweiterte Auflage
Audience: Bachelor's and master's students, practicing computer scientists and engineers
Pages
- Front matter: 20
- Main content: 702
Illustrations
- Illustrations: 185
- Tables: 9
Keywords: Machine learning; artificial intelligence; deep learning; neuronal networks

eBook

Published: January 19, 2022
ISBN: 9783110740196

Paperback

Published: January 31, 2022
ISBN: 9783110740141