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020			_a9789811519673 _9978-981-15-1967-3
024	7		_a10.1007/978-981-15-1967-3 _2doi
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100	1		_aZhou, Zhi-Hua. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aMachine Learning _h[electronic resource] / _cby Zhi-Hua Zhou.
250			_a1st ed. 2021.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021.
300			_aXIII, 459 p. 137 illus., 68 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1 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.
520			_aMachine 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.
650		0	_aMachine learning.
650		0	_aData mining.
650		0	_aComputer science _xMathematics.
650	1	4	_aMachine Learning.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aMathematics of Computing.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811519666
776	0	8	_iPrinted edition: _z9789811519680
776	0	8	_iPrinted edition: _z9789811519697
856	4	0	_uhttps://doi.org/10.1007/978-981-15-1967-3
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