000			02664cam a22003978i 4500
001			22096212
005			20251023092128.0
008			210622s2022 enk b 001 0 eng
010			_a 2021030297
020			_a9781108843607 _q(hardback)
020			_z9781108919371 _q(epub)
035			_a22096212
040			_aDLC _beng _erda _cDLC
042			_apcc
050	0	0	_aQ325.5 _b.L56 2021
082	0	0	_a006.3/1 _223
100	1		_aLindholm, Andreas _eauthor. _9876
245	1	0	_aMachine learning : _ba first course for engineers and scientists / _cAndreas Lindholm et al.
263			_a2111
264		1	_aCambridge, UK; _bCambridge University Press, _c2022.
300			_axii, 338 pages.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a"This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning"-- _cProvided by publisher.
650		0	_aMachine learning. _9614
700			_aWahlstrom, Niklas _eauthor. _9877
700			_aLindste, Fredrik _eauthor. _9878
700			_aSchon, Thomas B _eauthor. _9879
776	0	8	_iOnline version: _aLindholm, Andreas, 1989- _tMachine learning _b1. _dNew York : Cambridge University Press, 2021 _z9781108919371 _w(DLC) 2021030298
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2udc _cBK _h681.32.091 LIN _k681.32.091 _mLIN
999			_c89199 _d89199