000			02424cam a22003017a 4500
999			_c13933 _d13933
001			21059062
005			20251024154210.0
008			190709t20202020enka b 001 0 eng d
010			_a 2019945424
020			_a9780198828044
020			_a0198828047
035			_a(OCoLC)on1140387307
040			_aVJK _beng _cVJK _dBDX _dDLC
042			_alccopycat
050	0	0	_aQ325.5 _b.T73 2020
082	0	4	_aGrad 006.31 _bT774 2020
100	1		_aTrappenberg, Thomas P., _eauthor.
245	1	0	_aFundamentals of machine learning / _cThomas P. Trappenberg, Dalhousie University.
250			_aFirst edition.
264		1	_aOxford, United Kingdom : _bOxford University Press, _c©2020.
300			_axi, 247 pages : _billustrations
504			_aIncludes bibliographical references and index.
520			_aMachine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.-- _cSource other than the Library of Congress.
650		0	_aMachine learning.
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK