Artificial Intelligence with a Foundation Year - BSc (Hons)

Within the Computer Systems Module, you will gain knowledge and experience of computers and computer hardware.

This module presents a holistic view of how computer systems work and it also provides the underpinning knowledge required:

• for the design of computer architecture
• to show how software interacts with hardware
• to apply electronics principles
• to use number systems for computer technology

A foundation in mathematics is essential to understand AI models, their evaluation and limitations, especially when appraising new developments in the field. This module will cover the required algebra, statistics and matrix operations used by these models.

Data structures and algorithms are essential in computer science, software engineering, and computer games and graphics programming. Data structures are structured representations of data; the design of a data structure determines how operations (such as reading to, writing from, modifying, or computing with the data) can be achieved. An algorithm is a set of instructions which can be followed in order to solve a computational problem.

Students will undertake a major project involving research and application of that research in the solution of systems-related problems.

Knowledge and understanding are acquired though a mixture of formal lectures, tutor-led seminars and practical activities, with other independent learning activities at all stages. Emphasis is placed on guided, self-directed and student-centred learning, aimed at increasing independence of approach, thought and process.

The module provides access to effective commercial development environments and ensures students have practical awareness of computer systems requirements. Students are required to meet strict deadlines, and to manage and plan their overall workload.

Knowledge is assessed by several methods, including seminars, coursework, viva, presentation, interactive automated assessment, formal examination, and project work. Assessment criteria are published both at a generic course level and to provide guidance for individual items of assessment. Anonymous marking systems are in place for all formal examinations.

This introductory computer programming module provides the underpinning knowledge and practice for computing students to design, build and test software components. The module will make use of practical sessions primarily to allow you to apply programming principles and constructs in order to creatively solve problems by means of developing small programs. Module content and assessment enables learners to acquire programming skills in a modern imperative language.

This module covers the basic principles of artificial intelligence systems, techniques and the types of problems that can be solved by such techniques. This module also evaluates the risks and benefits of AI adoptions in different domains. Students learn about the philosophy of AI and how knowledge is represented.

The module utilises a hands-on approach to teaching. Teaching sessions include experiential activities such as critique of example scenarios; these scenarios will include situations tailored to real world case studies.

Programming is an engineering tool that plays a vital role to drive most of the modern technologies surrounding us, including the technological devices for communication, transportation and entertainment. In other words it can be said that our modern lifestyles are heavily dependent on programming.

Moreover, businesses increasingly rely on computers and the software run on them. Programming skills and a broader and deeper understanding of programming are therefore becoming increasingly important to the jobs market.

During the module you will work to design, develop, use and evaluate a “backend service” system backed by a database. You will produce this system over the course of the semester through engaging with the coursework throughout various milestones, developing a “portfolio” of work as you go along.

The module will be taught using a combination of lectures and practical, interactive lab sessions for learning skills and gaining important feedback. Independent learning will also be supported by a range of materials and activities delivered using Moodle.

Artificial Intelligence (AI) is a core component of computer science, aiming at developing intelligent agents that mimic human’s cognitive capability in learning, reasoning, and problem solving.

As a branch of AI, machine learning (ML) allows to create software that adapts and learns (from examples), rather than being explicitly programmed for a particular outcome. Both AI and ML rely on managing, processing and analysis of large datasets, something that Data Science (DS) is concerned with. Many services provided by technology giants such as Google, Microsoft, IBM, Facebook, Amazon, etc. are powered by DS, ML and AI. The recent advances in these subjects have already led to significant industrial applications such as self-driving cars and Industry 4.0.

This module covers the rapidly growing area of Machine Learning Operations (MLOps) and its coupling with the management and governance of data.

Data management aims to minimise the risk and cost of storing and utilising data in an efficient manner that ensures regulatory compliance, while avoiding legal complications, and security breaches. It also entails the provision of efficient access to accurate data when and, where it is needed, to facilitate effective provision of data-powered services and data-driven decision making.

Machine Learning Operations (MLOps) is the Data Science equivalent to the term “DevOps” used in Software Engineering, namely it covers appropriate practices and strategies for organisations to successfully apply Machine Learning techniques in a structured, explainable, repeatable and, importantly, production-ready manner.

Image analysis is an integral part of many commercial AI systems for tasks such as object detection, depth estimation, tracking and image generation. This module will teach the relevant concepts using open-source libraries and algorithms to create practical vision systems.

Natural Language Processing (NLP) is a rapidly developing field with broad applicability throughout the hard sciences, social sciences, and the humanities. The ability to harness, employ and analyse linguistic and textual data effectively is a highly desirable skill for academic work, in government, and throughout the private sector.

This module is intended as a theoretical and methodological introduction to the most widely used and effective current techniques, strategies, and toolkits for natural language processing.

The module will also consider how harnessing large digital corpora and large-scale textual data sources has changed how scholars engage with and evaluate digital archives and textual sources, and what opportunities textual repositories offer for computational approaches to the study of literature, history, and a variety of other fields, including law, healthcare, business, and the social sciences.

This module is designed to provide you with the opportunity to undertake a credit bearing, 40- week Professional Placement as an integral part of your Undergraduate Degree. 

The purpose of the Professional Placement is to improve your employability skills which will, through the placement experience, allow you to evidence your professional skills, attitudes and behaviours at the point of entry to the postgraduate job market. Furthermore, by completing the Professional Placement, you will be able to develop and enhance your understanding of the professional work environment, relevant to your chosen field of study, and reflect critically on your own professional skills development within the workplace.

Deep learning (also called deep neural network) is a sub-field of Artificial Intelligence (AI), aiming at extracting knowledge automatically from large data sets in a hierarchical way.

Deep learning, which is considered as the state-of-the-art in data science and AI, is inspired by the structure of human brain. Now well established and accepted in industry, deep learning provides both predictive and descriptive analytics solutions that can be applied to a wide range of applications from business to scientific and governmental applications.

As artificial intelligence gets widely adopted, its societal implications correspondingly deepen and broaden. Therefore, this module teaches the ethical implications of using artificial intelligence and will invite a critical assessment of such implications in the assessed work.

This module will introduce NoSQL databases and distributed data storage frameworks through practice based activities. Through guided hands-on tutorials, you will become familiar with techniques using non-structured and semi-structured data examples. Also, you will gain knowledge on managing and processing data in a distributed infrastructure. This module will improve your development skills and provide experience with many different data systems. Also, students will gain knowledge about protection of the data in NoSQL systems.

Generative AI refers to a class of artificial intelligence that can produce original content — such as text, images, code, and audio — based on patterns it has learned from existing data. It is the technology behind tools like ChatGPT, Copilot, and others that are reshaping how we work, communicate, and create. This module offers a clear and practical introduction to generative AI. It explores how generative AI works, what it can realistically achieve, and where its limitations lie. Large Language Models, AI agents and common Generative projects will be covered.

One of the major objectives of the computer science course is to introduce the theory, principles and technologies underlying the construction of modern computing systems. This module introduces Cloud Computing, which is the technology that enables on-demand computing resources (everything from applications to data centres) over the Internet.

This technology has revolutionised modern computing by allowing users to access infinite pool of computing services and resources on a pay-per-use basis. This module introduces the fundamental concepts and technologies related to cloud computing architecture, platforms and services. The module follows a practical approach to equip the students with hands-on experience of cloud computing by using a public Cloud service, such as Amazon Web Services.

The purpose of the module is to enable you to undertake a sustained, in-depth and research-informed project exploring an area that is of personal interest to you. In agreement with your supervisor, you will decide upon your topic which will take the form of a practical outcome (artefact) with accompanying contextual material. The main consideration when choosing your topic is that it must be aligned to the programme you are studying, and you should consider the relevance of this topic to your future academic or professional development.

At this level, you will be expected to work independently but you will receive additional one-to-one support from your supervisor, who will be familiar with your chosen topic area. As you progress on the module, extra support will be available and this may take the form of group seminars, workshops and online materials that will help to develop your project.

Knowledge and understanding are acquired though a mixture of formal lectures, tutor-led seminars and practical activities, with other independent learning activities at all stages.

Emphasis is placed on guided, self-directed and student-centred learning, aimed at giving you increasing independence of approach, thought and process.

The course provides access to effective commercial development environments and ensures you have practical awareness of computer systems requirements. You are required to meet strict deadlines, and to manage and plan your overall workload.

Knowledge is assessed by a number of methods, including seminars, coursework, viva, presentation, interactive automated assessment, formal examination and project work. Assessment criteria are published both at a generic course level and to provide guidance for individual items of assessment. Anonymous marking systems are in place for all formal examinations.

You will undertake a major project involving research and application of that research in the solution of systems-related problems.