Machine Learning module (CS51009)

Machine learning powers many of today’s most advanced technologies — from medical diagnosis and self-driving cars to recommendation systems and predictive analytics. This module introduces you to the fundamental ideas behind how computers learn patterns from data and make predictions or decisions without being explicitly programmed.

You’ll explore the key supervised learning techniques used across industry and research, including linear models, logistic regression, and neural networks. Alongside learning the underlying theory, you’ll gain hands-on experience training and evaluating models using modern software frameworks. The module bridges the gap between theory and practice, preparing you to apply machine learning techniques confidently to real-world problems.

What you will learn

In this module, you will:

• study supervised machine learning techniques for classification and regression
• explore how algorithms learn from data through generalisation and regularisation
• gain practical experience training models using real-world datasets
• learn how to evaluate model performance using cross-validation and error analysis
• develop an understanding of neural networks and deep learning principles

By the end of this module, you will be able to:

• explain the theoretical foundations of supervised learning
• implement and compare key machine learning algorithms
• design and evaluate a complete machine learning experiment
• interpret model results and communicate findings effectively through technical reporting

Assignments / assessment

• supervised machine learning coursework (50%)
• final written exam (50%)

Teaching methods / timetable

You will learn through a combination of lectures, tutorials, and lab-based sessions.
Tutorials will focus on exploring algorithms conceptually, while practical labs will allow you to experiment with machine learning frameworks and datasets. Academic staff and tutors provide hands-on guidance to help you connect theoretical knowledge with practical application.