LEADER 01054nas 2200337 c 450 001 9911049118103321 005 20260107165625.0 035 $a(CKB)44565703500041 035 $a(EXLCZ)9944565703500041 100 $a20260107a19999999 u| | 101 0 $ager 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInfo Bulletin$fVerein Schweizer Armeemuseum = Info bulletin / Musée suisse de l'armée 210 1$aThun$cVerein Schweizer Armeemuseum VSAM$d1999 215 $aonline Ressource$cIllustrationen 517 1 $aInfo bulletin / Musée suisse de l'armée 606 $aArmee$2gnd$3(DE-588)4143024-4 606 $aMuseum$2gnd$3(DE-588)4040795-0 607 $aSchweiz$2gnd 608 $aZeitschrift$2gnd-content 608 $aPériodique.$2idref 608 $aPeriodici$2sbt12-content 615 7$aArmee 615 7$aMuseum 712 02$aVerein Schweizer Armeemuseum$4isb 801 0$bCH-ZuSLS RZH E32 912 $a9911049118103321 996 $aInfo-Bulletin$94436887 997 $aUNINA LEADER 04187nam 2200433z- 450 001 9910984678103321 005 20250319080001 010 $a9780128188040 010 $a0128188049 010 $a9780128188033 010 $a0128188030 035 $a(CKB)4100000010350981 035 $a(MiAaPQ)EBC6118601 035 $a(VLeBooks)9780128188040 035 $a(FR-PaCSA)88963244 035 $a(FRCYB88963244)88963244 035 $a(EXLCZ)994100000010350981 100 $a20260104d2020 u| | 101 0 $aeng 135 $aurun| ||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning$eA Bayesian and Optimization Perspective$fTheodoridis, Sergios 205 $aSecond edition 210 1$aCambridge, MA, USA$cAcademic Press$d2020 215 $a1 online resource (xxvii, 1031 pages) ;$cillustrations 311 08$a9780128188033 320 $aIncludes bibliographical references and index. 330 $a Machine Learning: A Bayesian and Optimization Perspective, 2nd edition , gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aTheodoridis$b Sergios$0299259 801 0$bFR-PaCSA 906 $aBOOK 912 $a9910984678103321 996 $aMachine learning$91909061 997 $aUNINA