Course Catalogue

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A practical introduction to the foundations of machine learning.

3 hours per week.

Mandatory: Working knowledge of mathematics (e.g., matrices, linear spaces and basic geometry, as covered in, for example, Math1RS or Math1RT).

Options: Some experience in probability and statistics would be useful but is not essential.

Machine Learning (H)

None

Examination 50%, coursework 50%.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The coursework cannot be redone because the feedback provided to the students after the original coursework would give any students redoing the coursework an unfair advantage.

To present students with an introduction to the general theory of learning from data and to a number of popular Machine Learning methods.  To present students with the practical application of Machine Learning techniques in a variety of domains, including Human Computer Interaction, Information Retrieval, Bioinformatics and Computer Vision and Graphics.

By the end of the course students will be able to:

1. Demonstrate knowledge of the major machine learning application areas in, for example Information Retrieval, Human Computer Interaction, Bioinformatics and Computer Vision & Graphics;

2. Explain the principle of learning from data;

3. Implement and use machine learning algorithms in Python;

4. Apply the main machine learning methods: regression, classification, clustering, probability density estimation and dimensionality reduction;

5. Contrast the strengths and weaknesses of different algorithms for different tasks and datasets;

6. Analyse the benefits and drawbacks of some advanced machine learning approaches, e.g. non-parametric methods, sampling techniques and neural networks.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.