DATA70132: Statistics & Machine Learning 2: AI, Complex Data, Computationally Intensive Statistics (Semester B, 2024/25 Academic Year)

The module is delivered as a mixture of lectures and practical sessions and has five main sections:

1. Dimension reduction and feature extraction: principal components analysis, feature selection, information theory.
2. Classifiers and clustering: supervised and unsupervised learning, k-means and k-nearest neighbours, agglomerative clustering and dendrograms, support vector machines, linear and quadratic discriminants, Gaussian process classification, model-based clustering, mixture models and the EM algorithm.
3. Neural Networks and Deep Learning: perceptrons, back-propagation and multi-layer networks.
4. Markov-chain Monte Carlo (MCMC) methods: Markov chains and their stationary distributions, likelihood-based inference using the Metropolis-Hastings algorithm, likelihood-free inference using Approximate Bayesian Computation, tests for convergence, applications to Bayesian inference.
5. Special Topic: Depending on the teaching staff, one special topic will be chosen to go into near-research depth, e.g. Random Forests; Social Networks; Time Series Analysis; Advanced Monte Carlo methods.

See details here