Postgraduate Taught

Artificial Intelligence and Machine Learning:

Artificial Intelligence and Machine Learning are transforming industries ranging from healthcare and finance to robotics and autonomous systems. This specialism provides students with both foundational and advanced knowledge of modern AI and machine learning techniques, enabling them to design, develop, and deploy cutting edge intelligent systems for real-world applications.

Core Modules

CS920 Foundations of Artificial Intelligence and Machine Learning (Term 1)

This module introduces the fundamental concepts that underpin modern AI and machine learning. Students explore supervised and unsupervised learning techniques before progressing to reinforcement learning and other core AI methodologies. It provides an excellent foundation for students considering further specialisation in the field.

CS921 Artificial Intelligence and Machine Learning (Term 2)

Building on the foundations established in Term 1, this module provides an in-depth exploration of advanced AI and machine learning techniques. Students examine state-of-the-art approaches used across research and industry, gaining the knowledge required to tackle complex real-world challenges in intelligent systems.

Related Optional Modules

• Computational Biology
• Natural Language Processing
• Agent Based Systems

For a detailed overview of the course - including entry requirements, modules, assessment, and teaching, how to apply, as well as information about fees and funding, click the button below.

Cyber Security:

As organisations become increasingly dependent on digital infrastructure, the demand for cyber security expertise continues to grow. This specialism combines theoretical foundations with practical experience across areas such as cryptography, software security, hardware security, network security, and web security. Students gain hands-on experience in analysing vulnerabilities, understanding attacks, and developing secure systems.

Core Modules

CS930 Foundations of Cyber Security (Term 1)

This module introduces the fundamental concepts in security, including security principles, elementary cryptography, authentication, Operating System security, software security, web security, and network security. The aim is to enable a broad understanding of computer and internet security in a real-world context. It serves as an introductory course to first-year MSc students who have no or limited security background.

CS931 Advanced Cyber Security (Term 2)

Building on the Foundations of Cyber Security module, this module provides an advanced course for MSc students in computer and internet security. It aims to provide an in-depth understanding of core security topics, including cryptography, software security, web security and network security. Students will take a hands-on approach to learn how to attack and defend security systems.

Related Optional Modules

• Quantum Computing
• Digital Forensics

For an overview of the optional modules, click here.

For a detailed overview of the course - including entry requirements, modules, assessment, and teaching, how to apply, as well as information about fees and funding, click the button below.

Data Analytics:

Data has become a critical asset across every sector of society. The Data Analytics specialism equips students with the computational and analytical skills required to manage, analyse, and interpret complex datasets. Students learn techniques spanning the entire data lifecycle, from data collection and visualisation to advanced modelling and insight generation, preparing them to solve real-world data-driven challenges.

Core Modules

CS950 Foundations of Computational Data Analytics (Term 1)

Students are introduced to key concepts and techniques for data analysis with diverse types, including tabular, text, time series, and geospatial data. Students will gain practical skills in data cleaning, dimensionality reduction, visualisation, and exploratory analysis, as well as methods and algorithms for managing and analysing real-world datasets efficiently. It serves as a taster for students considering further study in the Computational Data Analytics module.

CS951 Computational data Analytics (Term 2)

This advanced module explores state-of-the-art techniques for analysing complex, high-dimensional, and multimodal data at scale. Students will apply modern methods in data integration, embedding, visualisation, and privacy-preserving analytics using scalable tools. The module aims to deepen students' understanding of applied machine learning and data infrastructure in practical, data-intensive environments.

Related Optional Modules

• Computational Biology
• Natural Language Processing

For an overview of the optional modules, click here.

For a detailed overview of the course - including entry requirements, modules, assessment, and teaching, how to apply, as well as information about fees and funding, click the button below.