The Resource Machine learning : a Bayesian and optimization perspective, Sergios Theodoridis

# Machine learning : a Bayesian and optimization perspective, Sergios Theodoridis

Label
Machine learning : a Bayesian and optimization perspective
Title
Machine learning
Title remainder
a Bayesian and optimization perspective
Statement of responsibility
Sergios Theodoridis
Creator
Subject
Language
eng
Summary
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
OPELS
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
• dictionaries
• bibliography
Machine learning : a Bayesian and optimization perspective, Sergios Theodoridis
Label
Machine learning : a Bayesian and optimization perspective, Sergios Theodoridis
Publication
Related Contributor
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Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Dimensions
unknown
{'f': 'http://opac.lib.rpi.edu/record=b4171177'}
Extent
1 online resource (xxi, 1050 pages)
File format
unknown
Form of item
online
Isbn
9780128017227
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote

#### Library Locations

• Folsom Library
110 8th St, Troy, NY, 12180, US
42.729766 -73.682577