This unit provides students with advanced knowledge in neural networks and related pattern recognition techniques. Main topics include: introduction to pattern recognition; discriminative versus generative approaches; multilayer perceptrons; radial basis function networks; support vector machines; mixture models and the Expectation-Maximization algorithm; Bayesian methods; support vector machines, and Kohonen Networks. Case studies in bioinformatics and document categorization are included. Assignments offer students a sufficiently large space to explore neural networks and related pattern recognition techniques in terms of both theoretical issues and applications in the real world.

Subject rules