000			02022nam a22002417a 4500
003			OSt
005			20260714112035.0
008			260714b |||||||| |||| 00| 0 eng d
020			_a978-1108940023
020			_a1108940021
041			_aeng
082			_a006.31 JIA-M
100			_aJiang, Hui. _988896
245			_aMachine learning fundamentals : a concise introduction
260			_aCambridge ; _bCambridge University Press, _c2021.
300			_axviii, 373p.
520			_aMachine Learning Fundamentals: A Concise Introduction provides an accessible introduction to the principles and techniques of modern machine learning. The book explains the mathematical foundations of supervised and unsupervised learning, probabilistic models, optimization methods, neural networks, deep learning, and statistical learning theory. Practical examples and concise explanations make it suitable for undergraduate and graduate students as well as researchers seeking an introduction to machine learning.
520			_aThis lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.
650			_aMachine learning. _988897
650			_aArtificial intelligence. _988898
942			_cBK
999			_c200818 _d200818