ITO5201 · Machine learning

Overview

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 and generative models, k-means and latent variable models (e.g. Gaussian mixture model), expectation-maximisation, neural networks and deep learning, and principles in scaling typical supervised and unsupervised learning algorithms to big data using distributed computing.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

In-semester assessment: 100%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Describe what statistical machine learning and its theoretical concepts are;
2. Assess a typical machine learning model and algorithm;
3. Develop, and apply major models and algorithms for statistical learning;
4. Scale typical statistical learning algorithms to learn from big data.

Workload

A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

Student experience, not official rules. Nothing here changes what the Handbook says.