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.