Advanced Computer Science (Artificial Intelligence) MSc

The following modules are available in 2022/23 for the Advanced Computer Science (Artificial Intelligence) MSc and are examples of the modules you are likely to study. All Modules are subject to change. You will study 180 credits in total.

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.

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.

Machine Learning – 15 credits

On completion of this module, students should be able to: list the principal algorithms used in machine learning, and derive their update rules; appreciate the capabilities and limitations of current approaches; evaluate the performance of machine learning algorithms; use existing implementation(s) of machine learning algorithms to explore data sets and build models.

Optional modules include:

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.

Advanced Software Engineering - 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.

Data Mining and Text Analytics - 15 credits
Introduction to linguistic theory and terminology. Understand and use algorithms and resources for implementing and evaluating text mining and analytics systems. Develop solutions using open-source and commercial toolkits. Consider the applications of data mining and text analytics through case studies in information retrieval and extraction.

S cientific Computation - 15 credits
Understand the range of problems that can be formulated as nonlinear equation systems.Consider standard algorithms for these problems and the efficiency of their implementation.Demonstrate how state-of-the-art algorithms deliver gains in efficiency and allow the solution of large, sparse systems of nonlinear equations.

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