Advanced Computer Science (Artificial Intelligence) MSc

You will study 180 credits in total during your Advanced Computer Science (Artificial Intelligence) MSc. A standard module is typically worth 15 credits and the research project is worth 60 credits. These are the modules studied in 2019. If you are starting in September 2020, these will give you a flavour of the modules you are likely to study. All Modules are subject to change.

Compulsory modules

MSc project - 60 credits
You will undertake a research project during the summer months.

Recent projects include:

  • Ontology-enriched access to digital repositories
  • Relevance and trust in social computing for decision-making
  • Advanced GIS functionality for animal habitat analysis.

Bio-Inspired Computing - 15 credits
Introduces the use of natural systems as the inspiration for artificially intelligent systems. This module covers the history, philosophy and application of bio-inspired computing, including swarm intelligence, neural networks and evolutionary design.

Knowledge Representation and Reasoning - 15 credits
The principal representations and algorithms used in machine learning and the techniques used to evaluate their performance. You will implement a challenging learning system using a publicly available pack of standard algorithms.

Image Analysis - 15 credits
Image analysis techniques are used in many applications, such as motion-controlled computer games, medical diagnosis and surgical guidance, autonomous systems, surveillance and security, and image content retrieval systems. In this module, you will learn about the current approaches to image processing and computer vision and study how these are applied in a number of different applications.

Optional modules include:

Big Data Systems - 15 credits
The aim of the module is for students to develop a practical understanding of methods, techniques and architectures needed to build big data systems required, so that knowledge may be extracted from large heterogeneous data sets.

Data Science - 15 credits
The aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

Parallel and Concurrent Programming - 15 credits
This module introduces you to the principles and practice of parallel and concurrent programming on shared memory architectures (both CPU and GPU). It covers the fundamental concepts underlying concurrency, in particular the complexity of managing shared resources and the language/data abstractions used to mediate interaction between threads of execution.

Cloud Computing - 15 credits
State-of-the-art approaches and solution strategies for designing, building and maintaining cloud applications. This module covers areas such as programming models, virtualisation and quality of service.

Semantic Technologies and Applications - 15 credits
Applications include linked data, semantic data browsers and smart social spaces (eg semantic wikis, semantic blogs, social networking). There will be a practical component with hands-on experience of applying semantic web technologies in a specific domain (eg decision-making, learning, health, e-business, digital libraries).

Scheduling - 15 credits
State-of-the-art approaches and solution strategies in designing practical scheduling optimisation algorithms. This module looks at a number of real-life problems and case studies from different domains such as transport, computer networks and healthcare.

The full list of module information can be read in the course catalogue.