Machine Learning

Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models, Third Edition presents the most updated information on topics including mean square, least squares, maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modeling 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.
In addition, dimensionality reduction and latent variables modeling are also considered in-depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. Finally, the book 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.

Sergios Theodoridis is professor of machine learning and signal processing with the National and Kapodistrian University of Athens, Athens, Greece and with the Chinese University of Hong Kong, Shenzhen, China.
He has received a number of prestigious 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 2017 European Association for Signal Processing
(EURASIP) Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Education Award, and the 2014 EURASIP Meritorious Service Award. He has served as president of EURASIP and vice president for the IEEE Signal Processing Society and as Editor-in-Chief IEEE Transactions on Signal processing. He is a Fellow of EURASIP and a Life Fellow of IEEE.
He is the coauthor of the best selling book Pattern Recognition, 4th edition, Academic Press, 2009 and of the book Introduction to Pattern Recognition: A MATLAB Approach, Academic Press, 2010.