Machine learning algorithms from a signal processing viewpoint; unsupervised learning (K-means, deterministic annealing, EM algorithm); supervised learning (Support Vector Machines, neural networks); regression; Bayesian inference and tracking using Markov chain Monte Carlo and sequential Monte Carlo (particle filter) techniques.