000			a
999			_c32236 _d32236
008			231101b xxu||||| |||| 00| 0 eng d
020			_a9781108843607
082			_a006.31 _bLIN
100			_aLindholm, Andreas
245			_aMachine learning : a first course for engineers and scientists
260			_bCambridge University Press, _c2022 _aCambridge :
300			_axii, 338 p. ; _bill., _c27 cm.
365			_b54.99 _cGBP _d107.60
504			_aIncludes bibliographical references and index.
520			_aThis 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.
650			_ak-NN
650			_aSupport vector machines
650			_aDeep neural networks
650			_aGaussian processes
650			_aPCA
650			_aAdversarial networks
650			_aLogistic regression
650			_aDecision trees
650			_aK-means
650			_aGenerative modeling
700			_aWahlström, Niklas
700			_aSchön, Thomas B.
700			_aLindsten, Fredrik
942			_2ddc _cBK