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