Artificial Intelligence with Foundation Year BSc (Hons)

This module is an introduction to probability theory and statistical methods. The module leads to a deeper understanding of probability distributions, random variables and their role in sampling. Tools such as hypothesis testing are presented and a basic introduction to the statistical software SPSS is provided.

The aim of this module is to extend your knowledge of matrices, vectors and systems of linear equations and to introduce the abstract concepts of vector spaces, linear maps and inner products.

The module provides a thorough grounding in the fundamentals of Java programming language and object programming concepts. It will focus on the design and build of Java desktop applications using the Java Development Kit and popular Integrated Development Environments, following established industry standard methodologies. The module will have a strong emphasis on using OO modelling techniques to interpret and implement business related applications.

This module will help you to gain the knowledge and competence to deal with basic data structures and algorithms. You will learn how to specify collections using abstract data types (ADTs) and to implement them using a variety of techniques such as linked lists and trees. You'll also use a range of algorithms, including searching and sorting.

This module aims to introduce students to information systems theory and to explore the way that databases underpin IS systems. The module will also examine the principles and techniques involved in implementing relational databases. It will cover database environments, database analysis, database design and will also give an introduction to database security and transaction management. Oracle will be used to explore the capabilities of SQL and to construct a small relational database.

This module will introduce you to the fundamentals of AI with a focus on

1. the structures, resources and processes that together make up an intelligent agent
2. the techniques, models and tools that can be used to simulate the “intelligent” processes
3. the skills and capabilities necessary to critically review AI literature and/or products, to synthesise ideas, to systematically solve AI problems and to communicate effectively.

You will gain the knowledge and skills required to understand the fundamentals of AI, to solve real world problem more “intelligently”, and ultimately to build intelligent artefacts.

Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including mathematical optimization, logic, probability, and many others. AI has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

You will gain the knowledge and understanding of fundamental concepts of computational theory and computational complexity. You will learn how to examine whether a given problem can be solved computationally.

The aim of this module is to provide you with the experience of working collaboratively as part of a project team. The module will provide opportunities for you to adopt different project team roles in order to deliver a solution for a real-world client.

This module is intended to cover fundamental theory and algorithms of machine learning, as well as recent research topics.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

This module will explore the advanced principles and techniques currently being used in real-world computer vision systems, and the research and development of new systems.

Computer vision lets computers gain high-level understanding from digital images or videos, and seeks to automate tasks that the human visual system can do. It has become ubiquitous with applications in search, image understanding, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, segmentation, localisation and detection.

In this module you will learn how digital images are formed, how they are represented and stored on computers, and how they can be processed by computers to extract semantic information. You will have the opportunity to develop algorithms for detecting interesting features in images, design convolutional neural networks to perform tasks such as image classification, and explore techniques for solving real-world problems such as object detection. Relevant programming language (e.g. Python, OpenCV, MATLAB, etc.) will be used for model developments.

This module is about human and technical aspects of interactive computing systems and organisations. In the course of taking this module, you'll consider the interplay between human users, designers, developers and computers. Therefore, its basis is in psychology and human factors as well as in software engineering and interaction design.