Neural computing, data mining, evolutionary computing, ensemble networks.

Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.

Visual perception with neural networks.

Bayesian inference, variational methods, graphical models, nonparametric Bayes.

Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.

Reinforcement learning.

Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.

Statistical methods for inference and learning.

Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.

Bayesian inference, Markov chain Monte Carlo simulation, machine learning.