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020 _a9789811519697
040 _aNISER LIBRARY
_beng
_cNISER LIBRARY
082 _a004.85
_bZHO-M
100 _aZhou, Zhi-hua
245 _aMachine learning
260 _aSingapore :
_bSpringer,
_c2021
300 _axiii, 458p.
504 _aIncludes index
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.
521 _aThe 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 _aMachine learning
650 _aNeural networks
650 _aBayesian networks
650 _aLearning algorithms
650 _aMathematical models
700 _aLiu, Shaowu
856 _3Table of contents
_uhttps://link.springer.com/content/pdf/bfm:978-981-15-1967-3/1
856 _3Reviews
_uhttps://www.goodreads.com/book/show/71469651-machine-learning#CommunityReviews
942 _2udc
_cBK
999 _c35376
_d35376