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Description
Aims:
To have a full understanding of the learning outcomes.
Intended learning outcomes:
On successful completion of the module, a student will be able to:
- Understand machine learning at both the theoretical and practical level.
- Solve real-world machine learning problems using the right tools.
Indicative content:
The following are indicative of the topics the module will typically cover:
Introduction to Supervised Learning
- Linear models for regression and classification: least squares, logistic regression.
- Concepts of overfitting and regularisation, L1 and L2 regularisation.
- Boosting, Decision Trees, Random Forests.
- Support Vector Machines.
- Deep Learning: Neural Networks for regression and classification, Convolutional Neural Networks, Recurrent Neural Networks.
Introduction to Unsupervised Learning
- K-means, Principal Components Analysis, Embeddings & Representation Learning.
- Expectation-Maximisation, Mixture of Gaussians, Hidden Markov Models.
- Deep Autoencoders, Generative Adversarial Networks.
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 formally available; (2) have an understanding of Calculus, Linear Algebra and Probability Theory; and (3) have proficiency in coding (preferably in Python).
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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