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Machine Learning
Translated by: Shaowu
Liu
Publisher: Springer Singapore
Hardcover ISBN:
978-981-15-1966-6, Published: 21 August 2021
Softcover ISBN:
978-981-15-1969-7, Published: 22 August 2022
Number of Pages: XIII, 459
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Provides a comprehensive and unbiased introduction to almost all aspects
of machine learning
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.