Artboard 33 Artboard 16 Artboard 18 Artboard 15 Artboard 21 Artboard 1 Artboard 2 Artboard 5 Artboard 45 Artboard 45 Artboard 22 Artboard 9 Artboard 23 Artboard 17? Artboard 28 Artboard 43 Artboard 49 Artboard 47 Artboard 38 Artboard 32 Artboard 8 Artboard 22 Artboard 5 Artboard 25 Artboard 1 Artboard 42 Artboard 11 Artboard 41 Artboard 13 Artboard 23 Artboard 10 Artboard 4 Artboard 9 Artboard 20 Artboard 6 Artboard 11 Artboard 7 Artboard 3 Artboard 3 Artboard 12 Artboard 25 Artboard 34 Artboard 39 Artboard 24 Artboard 13 Artboard 19 Artboard 7 Artboard 24 Artboard 31 Artboard 4 Artboard 14 Artboard 27 Artboard 30 Artboard 36 Artboard 44 Artboard 12 Artboard 17 Artboard 17 Artboard 6 Artboard 27 Artboard 19 Artboard 30 Artboard 29 Artboard 29 Artboard 26 Artboard 18 Artboard 2 Artboard 20 Artboard 35 Artboard 15 Artboard 14 Artboard 48 Artboard 50 Artboard 26 Artboard 16 Artboard 40 Artboard 21 Artboard 29 Artboard 10 Artboard 37 Artboard 3 Artboard 3 Artboard 46 Artboard 8
e-book

Machine Learning - A Bayesian and Optimization Perspective (Cód: 9753196)

Theodoridis, Sergios

ELSEVIER S&T

Ooops! Este produto não está mais a venda.
Mas não se preocupe, temos uma versão atualizada para você.

Ooopss! Este produto está fora de linha, mas temos outras opções para você.
Veja nossas sugestões abaixo!

R$ 291,28

em até 9x de R$ 32,36 sem juros

Total: R$0,00

Em até 1x sem juros de R$ 0,00


Origem

R$ 49,90

Crédito:
Boleto:
Cartão Saraiva:

Total: R$0,00

Em até 9x sem juros de R$ 0,00


Machine Learning - A Bayesian and Optimization Perspective

R$291,28

Descrição

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, 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. The book builds carefully from the basic classical methods  to  the most recent trends, with chapters 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 short courses on sparse modeling, deep learning, and probabilistic graphical models.All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling.Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

Características

Peso 0.00 Kg
Produto sob encomenda Sim
Marca ELSEVIER S&T
Idioma 337
Acabamento e-book
Proteção Drm Sim
Código do Formato Epub
Cód. Barras 9780128017227
AutorTheodoridis, Sergios