Machine learning / Zhi-Hua Zhou.
2021
Q325.5 .Z56 2021
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Online Access
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Unlimited
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Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Machine learning / Zhi-Hua Zhou.
ISBN
9789811519673 (electronic bk.)
9811519676 (electronic bk.)
9789811519666
9811519668
9811519676 (electronic bk.)
9789811519666
9811519668
Published
Singapore : Springer, [2021]
Copyright
©2021
Language
English
Language Note
Translated from Chinese.
Description
1 online resource : illustrations (chiefly color)
Item Number
10.1007/978-981-15-1967-3 doi
Call Number
Q325.5 .Z56 2021
Dewey Decimal Classification
006.3/1
Summary
Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed August 26, 2021).
Available in Other Form
Machine learning.
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Online Access
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Table of Contents
1 Introduction
2 Model Selection and Evaluation
3 Linear Models
4 Decision Trees
5 Neural Networks
6 Support Vector Machine
7 Bayes Classifiers
8 Ensemble Learning
9 Clustering
10 Dimensionality Reduction and Metric Learning
11 Feature Selection and Sparse Learning
12 Computational Learning Theory
13 Semi-Supervised Learning
14 Probabilistic Graphical Models
15 Rule Learning
16 Reinforcement Learning.
2 Model Selection and Evaluation
3 Linear Models
4 Decision Trees
5 Neural Networks
6 Support Vector Machine
7 Bayes Classifiers
8 Ensemble Learning
9 Clustering
10 Dimensionality Reduction and Metric Learning
11 Feature Selection and Sparse Learning
12 Computational Learning Theory
13 Semi-Supervised Learning
14 Probabilistic Graphical Models
15 Rule Learning
16 Reinforcement Learning.