Machine learning

This unit introduces machine learning and the major kinds of statistical learning models and algorithms used in data analysis. Learning and the different kinds of learning will be covered and their usage will be discussed. The unit presents foundational concepts in machine learning and statistical learning theory, e.g. bias-variance, model selection, and how model complexity interplays with model's performance on unobserved data. A series of different models and algorithms will be presented and interpreted based on the foundational concepts: linear models for regression and classification (e.g. linear basis function models, logistic regression, Bayesian classifiers, generalised linear models), discriminative, probabilistic, and generative models, non-parametric models (e.g., k-nearest neighbour, Gaussian process regression), k-means and latent variable models (e.g. Gaussian mixture model), expectation-maximisation, and neural networks and deep learning. Moreover, implementation techniques will be introduced and practiced that allow to practically implement the introduced algorithms in a scalable manner with robust and standardised interfaces.

Problem-based learning

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.