By Sergios Theodoridis

This educational textual content offers a unifying point of view on computer studying through overlaying both probabilistic and deterministic methods -which are in keeping with optimization recommendations – including the Bayesian inference procedure, whose essence lies in using a hierarchy of probabilistic versions. The e-book offers the main computing device studying tools as they've been constructed in several disciplines, similar to information, statistical and adaptive sign processing and laptop technology. concentrating on the actual reasoning at the back of the math, the entire a number of tools and strategies are defined extensive, supported by way of examples and difficulties, giving a useful source to the scholar and researcher for figuring out and making use of computing device studying concepts.

The ebook builds conscientiously from the elemental classical tools  to  the newest traits, with chapters written to be as self-contained as attainable, making the textual content appropriate for  different classes: trend attractiveness, statistical/adaptive sign processing, statistical/Bayesian studying, in addition to brief classes on sparse modeling, deep studying, and probabilistic graphical models.

  • All significant classical innovations: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and on-line studying, Bayesian type, choice timber, logistic regression and boosting methods.
  • The most up-to-date tendencies: Sparsity, convex research and optimization, on-line disbursed algorithms, studying in RKH areas, Bayesian inference, graphical and hidden Markov versions, particle filtering, deep studying, dictionary studying and latent variables modeling.
  • Case stories - protein folding prediction, optical personality attractiveness, textual content authorship identity, fMRI info research, switch element detection, hyperspectral picture unmixing, aim localization, channel equalization and echo cancellation, express how the speculation should be applied.
  • MATLAB code for the entire major algorithms can be found on an accompanying site, permitting the reader to scan with the code.

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Machine Learning: A Bayesian and Optimization Perspective (Net Developers) by Sergios Theodoridis

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