MA429      Half Unit
Algorithmic Techniques in Machine Learning

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Mutually exclusive courses:

This course cannot be taken with ST443 at any time on the same degree programme.

Additional requisites:

A basic knowledge of statistics and linear algebra is required. Students without any prior experience with Python should engage with online Python tutorials that will be provided, with assistance.

The course introduces fundamental machine learning methods for data analytics problems. Vast quantities of data are available today in all areas of business, science, and technology as well as social networks. The goal of data mining is to extract useful information from massive-scale data. The aim of this course is to equip students with theoretically grounded and practically applicable knowledge of the most important machine learning algorithms used for this task, as well as how they should be applied. Mathematics (e.g., optimisation, graph theory), computer science and statistics all play an important role.

For classification and regression problems, methods studied include naive Bayes, K-nearest neighbours, decision trees, support vector machines, and neural networks. The course will also cover unsupervised learning methods such as clustering. Ethical issues arising from machine learning are also discussed.

The methods are illustrated on practical problems arising from various fields. Students will make use of various machine learning and data mining packages in Python.

15 hours of seminars and 20 hours of lectures in the Autumn Term.

There will be a formative group project, in preparation for a similar summative project.

• James, Witten, Hastie, Tibshirani, An Introduction to Statistical Learning: with Applications in R (2016)
• Hastie, Tibshirani, Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd ed. (2009)
• Witten, Frank, Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd or 4th ed. (2016)

Exam (60%), duration: 120 Minutes in the Spring exam period

Project (40%)

The examination is critical to assessment. In order to pass this course, students need to achieve a mark of at least 50% in the examination. A fail mark in the exam will result in an overall fail mark for the course: it cannot be compensated by the marks in the other elements.

The summative project will be due before the start of the Spring Term.