Machine Learning / by Zhi-Hua Zhou.

Format:

Book

Author/Creator:

Zhou, Zhi-Hua., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Machine learning.

Data mining.

Computer science-Mathematics.

Machine Learning.

Data Mining and Knowledge Discovery.

Mathematics of Computing.

Local Subjects:

Machine Learning.

Data Mining and Knowledge Discovery.

Mathematics of Computing.

Physical Description:

1 online resource (XIII, 459 pages) : 137 illustrations, 68 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

978-981-15-1967-3

9789811519673

Access Restriction:

Restricted for use by site license.