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Description
Aims:
The module aims to Introduce students to the basics of machine learning while giving class-based examples of applications to areas of domain specialisation.
Intended learning outcomes:
On successful completion of the module a student will be able to:
- Understand elements of the fundamental concepts and mathematical basis of machine learning; apply practical machine learning software to perform data analysis tasks.
Indicative content:
General theory and mathematical foundations are presented in lectures while practical applications are presented in classes.
The following are indicative of the topics the module will typically cover:
- An introduction to machine learning tasks (unsupervised, supervised, reinforcement).
- Mathematical foundations (linear algebra, calculus, probability, statistics).
- Supervised Learning: including an exploration of some of the following: linear and polynomial regression, logistic regression, Naive Bayes, kernel methods, SVMs, decision trees, ensemble learning, neural networks, Gaussian processes.
- Unsupervised Learning: including an exploration of some of the following: PCA, manifold learning, k-means, Gaussian mixture models, EM algorithm.
Requisites:
To be eligible to select this module as an optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; and (2) should have experience of rudimentary programming and an awareness of standard results in fundamentals of linear algebra (vectors, matrices, eigenvectors /eigenvalues etc.), elements of probability theory (random variables, expectation, variance, conditional probabilities, Bayes rule etc.), elements of statistics (sample statistics, maximum likelihood estimation etc.), and calculus (real-valued functions, derivatives, Taylor series, integrals etc.). Results from these areas will be used, often without proof, throughout the module.
Self-assessment test:
Students should take , to assess their ability against the level of the module.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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