The first part of his tutorial provides a brief overview of the fundamental methods and applications of statistical machine learning. The other speakers will detail or built upon this introduction.

Statistical machine learning is concerned with the development of algorithms and techniques that learn from observed data by constructing stochastic models that can be used for making predictions and decisions.

Topics covered include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimization; overfitting, regularization, and validation.