Machine Learning: A Bayesian and Optimization Perspective
- 20h 10m
- Sergios Theodoridis
- Elsevier Science and Technology Books, Inc.
- 2015
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.
About the Author
Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens. He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach. He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic Press Library in Signal Processing.
He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE.
In this Book
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Notation
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Introduction
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Probability and Stochastic Processes
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Learning in Parametric Modeling—Basic Concepts and Directions
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Mean-Square Error Linear Estimation
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Stochastic Gradient Descent—The LMS Algorithm and its Family
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The Least-Squares Family
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Classification—A Tour of the Classics
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Parameter Learning—A Convex Analytic Path
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Sparsity-Aware Learning—Concepts and Theoretical Foundations
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Sparsity-Aware Learning—Algorithms and Applications
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Learning in Reproducing Kernel Hilbert Spaces
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Bayesian Learning—Inference and the EM Algorithm
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Bayesian Learning—Approximate Inference and Nonparametric Models
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Monte Carlo Methods
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Probabilistic Graphical Models—Part I
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Probabilistic Graphical Models—Part II
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Particle Filtering
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Neural Networks and Deep Learning
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Dimensionality Reduction and Latent Variables Modeling
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