Artificial Intelligence BSc (Hons)

This teaching module aims to provide students with an introduction to different kinds of data and the means by which it can be collected, stored, retrieved, analysed and then communicated in order to achieve the goal of satisfying user information needs.

To introduce students to core mathematical concepts essential for studying artificial intelligence and computer science. To support students in developing the foundational mathematical skills and confidence required for the study of artificial intelligence and computer science.

To extend students’ understanding of mathematical methods relevant to AI. To develop confidence in applying mathematical techniques to AI-related problems and prepare students for more advanced quantitative study. 

This module aims to provide students with a comprehensive understanding of how knowledge can be represented, structured, and manipulated in artificial intelligence systems. Students will explore key methods for modelling and reasoning with knowledge. Emphasis is placed on how to acquire, formalise, and manage knowledge in a way that supports intelligent behaviour in machines. Through practical work and case studies, students will gain experience in designing and implementing knowledge-based systems, and will reflect critically on their strengths and limitations.

This module aims to provide knowledge and skills needed to model problems using mathematics and logic, to transform such models into versions which are amenable to computational solutions, and to give a basis for how to select the appropriate modelling methods and computational paradigms in order to achieve practical computational solutions.  

To develop a sound foundation in the way that computers operate and can be used to facilitate a variety of tasks and operations. 

To provide students with basic level of programming competence for AI.  

To provide opportunities for students to apply fundamental programming concepts as a solution to a non-trivial AI problem. 

To deepen students’ mathematical knowledge and analytical skills in areas critical to modern AI. The module emphasises mathematical reasoning, abstraction, and practical application in AI system development and evaluation.

This course provides an understanding of a set of useful data abstractions and algorithms for AI. It aims to stimulate students' critical thinking and develop their ability to choose appropriate AI algorithms in solving practical problems and implementing them in software. 

To provide students with the core concepts, tools, and techniques of data science, enabling them to collect, process, analyse, and interpret data effectively. The module focuses on developing practical skills in data wrangling, statistical analysis, and visualisation, and introduces students to data-driven decision-making and the role of data science in AI applications.

The goal of this course is to equip students with knowledge and comprehension of various valuable data structures and algorithms for AI. It aims to stimulate students' critical thinking and enhance their skills in evaluating the trade-offs involved in selecting the most suitable AI algorithms for specific purposes. 

To introduce students to the foundational principles, algorithms, and practical applications of machine learning. The module develops students’ understanding of supervised and unsupervised learning techniques, and builds their ability to design, implement, and evaluate machine learning models for real-world problem solving in artificial intelligence.

To develop skills required for planning and obtaining employment, whether it is an internship, a placement or a graduate job, in a field related to the student degree programme. To see the development of these skills as a continuing, managed, lifelong process.

To apply techniques, methods, algorithms and/or theories to an applied problem cognate to the field of MDS.

To develop skills required for planning and obtaining employment, whether it is an internship, a placement or a graduate job, in a field related to the student degree programme. To see the development of these skills as a continuing, managed, lifelong process. To apply techniques, methods, algorithms and/or theories to an applied problem cognate to the field of MDS.

The module aims to develop the knowledge and skills necessary for the design and implementation of software systems using recognised methodology, tools and technologies.  The module provides an introduction to software engineering and follows a development process from requirements and design through to implementation, creating a number of software artefacts. 

This module provides an opportunity for students to research advanced and debatable issues in Artificial Intelligence. Students will be able to learn about a range of advanced topics and to engage in discussion of critical issues through associated seminars. An advanced topic is one that is related to the area of Artificial Intelligence, will typically be a current area or issue of concern, but is not explicitly covered in this level of detail elsewhere in the course. The module seeks to enable the student to develop skills: researching and synthesising an advanced topic from academic and practical perspectives; and critical analysis.

The project enables the students to demonstrate an integrated approach to the topics covered in the degree programme. In particular: 

• acquire analytical and problem-solving skills
• recognise and define a problem to be explored and the work to be completed through experiential rather than passive learning 
• develop skills for independent research and development 
• develop the ability to describe and solve a problem in such a way that it can be measured 
• adapt and apply academic knowledge and skills in the investigation and solution of a problem

This module introduces deep learning architectures, which are key components of modern artificial intelligence. It covers both core and advanced architectures, along with their associated training methods. Recent developments and emerging trends in the field will also be explored.

Students will gain a deep understanding of how deep learning architectures function, enhance their ability to critically evaluate deep learning techniques, and apply them to real-world problem-solving scenarios.

To equip students with a solid understanding of the principles of experimental design and performance evaluation in the context of artificial intelligence. The module develops students' knowledge of relevant theories and statistical methods, and builds practical skills in designing experiments, analysing results, and interpreting data to support robust and reliable conclusions. 

Provide students with a strong theoretical and practical foundation in generative AI and prompt engineering, as well as practical skills to adapt and apply generative AI and prompt engineering technologies to solve complex real-world problems.

This module will equip students with a critical understanding of the ethical, social and regulatory implications of artificial intelligence. It encourages responsible design, development, and deployment of AI systems by integrating principles of fairness, accountability, transparency, regulations and trust and security into technical practice.