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Machine Learning: A Bayesian and Optimization Perspective, 2^nd 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.

Machine learning --- Bayesian statistical decision theory. --- Mathematical optimization. --- Mathematical models. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Learning, Machine --- Artificial intelligence --- Machine theory --- Apprentissage automatique --- Théorie de la décision bayésienne. --- Optimisation mathématique. --- Mathematics. --- Mathématiques.

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Matlab booklet to accompany Theodoridis, Pattern Recognition 4e. Contains tutorials, examples, and Matlab code corresponding to chapters from the Pattern Recognition text.*Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition 4e.*Solved examples in Matlab, including real-life data sets in imaging and audio recognition*Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

Pattern recognition systems. --- Pattern recognition systems --- Numerical analysis. --- Mathematics. --- MATLAB. --- Mathematical analysis --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- MATLAB (Computer program) --- MATLAB (Computer file) --- Matrix laboratory

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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.

Numerical methods of optimisation --- Operational research. Game theory --- Mathematical statistics --- Artificial intelligence. Robotics. Simulation. Graphics

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Artificial intelligence. Robotics. Simulation. Graphics --- Pattern recognition systems --- Reconnaissance des formes (Informatique) --- 681.3*I52 --- 519.7 --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Design methodology: classifier design and evaluation; feature evaluation and selection; pattern analysis (Pattern recognition) --- Mathematical cybernetics --- Pattern recognition systems. --- 519.7 Mathematical cybernetics --- 681.3*I52 Design methodology: classifier design and evaluation; feature evaluation and selection; pattern analysis (Pattern recognition)

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Pattern recognition systems --- Pattern perception --- Reconnaissance des formes (Informatique) --- Perception de structure --- Pattern recognition systems. --- Agrotechnology and Food Sciences. Information and Communication Technology --- Information and Communication Technology (General) --- Information and Communication Technology (General).