BSc Artificial Intelligence

COM1011: Fundamentals of Machine Learning

Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.

Co-requisite Modules: ECM1400, MTH1002, MTH1004, or equivalent.

This module is suitable for students with sufficient preparation in Mathematics and Programming.

This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.

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ECM1400: Programming

We use computers in almost all aspects of our daily lives and throughout science, so it is easy to take them for granted. However, in order that we can use computers to solve new problems and create new things, we have to be able to program them. This module introduces you to programming and problem solving with a computer.  You will learn how to formulate an algorithm to solve a problem, and you will acquire the skills to write, test and debug programs.

This module is an introductory course in computer programming and will introduce you to the fundamental concepts of computer algorithms and programming, with a strong emphasis on practical implementation. You will also learn how to apply analytical and problem-solving skills to the design and implementation of small applications.

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ECM1407: Social and Professional Issues of the Information Age

The module aims to provide you with the tools to reflect upon your role in the interface between digital technologies and society and on the moral and ethical use of information and information systems. By taking this module, you will become aware of your legal responsibilities and rights as an IT professional and as a user of digital technologies. The module will cover ethical theories, computer law and professional codes of conduct, and will address the ways in which broader areas of law (e.g. defamation, contracts, privacy and freedom of information legislation) impact upon technology users and IT professionals.

This module will introduce you to the law regulating the use of information and digital technology. It will enhance your awareness and critical thinking skills regarding the social impact of information technology, and help you relate professional codes of conduct to ethical theories.

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ECM1410: Object-Oriented Programming

This module will introduce you to object-oriented problem-solving methods and provide you with object-oriented (OO) techniques for the analysis, design and implementation of solutions. We will introduce you to these concepts, and you will develop skills with a new programming language. By the end of this module, you will be able to apply these skills to design and implement small applications.

The module aims to provide you with a thorough grounding in the fundamentals of object-oriented design concepts, alongside the fundamentals of the Java programming language, and general object-oriented design concepts. It will also introduce you to widely used components of the unified modelling language (UML), teach you how to interpret and implement a Java program from these higher-level designs, along with the pair programming approach used in industry.

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ECM1413: Computers and the Internet

This module is designed to equip you with the foundational information you need to understand and work in business and technical fields requiring the use of computers and networking technologies. Computing technology has a diversity of applications, so this module is suitable both for computer science students and for those pursuing other study disciplines. On this module, you will acquire foundational knowledge of computer systems (operating system and computer architecture) and computer networks.

By the end of the module, you should be well placed to make use of an extensive range of hardware and software technologies. In addition, you will have gained the knowledge and skills to enable you to analyse existing computer- and internet-based information systems.

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ECM1414: Data Structures and Algorithms

According to an old formula, Algorithms + Data Structures = Programs. This remains as true today as when it was originally formulated by Niklaus Wirth in 1976, and encapsulates the truism that all computation consists of the manipulation of data by means of systematic procedures. But data comes in many different forms (e.g., numerical, alphabetical, graphical) and only by knowing how it is structured can we specify the procedures – algorithms – for manipulating it to produce desired outcomes. Thus, the study of data structures and algorithms constitutes an integrated topic, which forms the subject matter of this module. You will be introduced to some of the key concepts in the area, with plenty of examples to illustrate them, and you will be given a chance to demonstrate your understanding by undertaking exercises. This module builds on the programming knowledge you have already acquired from ECM1400 Programming and will make use of mathematical tools introduced in ECM1415 (Discrete Mathematics for Computer Science) to enable data structures and algorithms to be described precisely.

Prerequisite module: ECM1400, ECM1415 or equivalent.

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ECM1415: Discrete Mathematics for Computer Science

Discrete mathematics is concerned with quantities which vary discretely, and because of that has an important role in Computer Science, in which discrete structures such as sets, graphs, lists, and trees play a fundamental role, and the underlying forms of reasoning are based on propositional and predicate logic rather than on calculus and mathematical analysis, with an emphasis on counting rather than measuring, e.g. enumerating permutations and combinations of objects satisfying specified conditions. This module will provide a thorough grounding in the fundamental structures and methods of discrete mathematics that are required for computer science.

The aim of this module is to provide you with the basic concepts and tools developed in discrete mathematics disciplines but needed for the study of computer science. As such, it forms an essential part of a rounded education of a computer scientist or computer expert whose work includes computer-based data manipulations.

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ECM1416: Computational Mathematics

Computer science draws from a wide range of essential mathematical techniques. This module will provide a solid foundation on the required mathematical tools and how to use them in solving computer science problems. This module will introduce linear algebra and vector spaces, statistics and probabilities and numerical optimization.  In the course of this module, you will learn to apply theoretical knowledge in concrete programming tasks.  This module complements previous mathematics module and is essential for all engaged in a Computer Science program.

In this module we aim to provide you with a foundation in the essential mathematical tools used in advanced computer science topics. We will teach you how to use vector and matrices, statistics and probabilities and numerical optimization methods and implement them in computer programs.

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ECM1425: Employability and Placement Preparation for Computer Scientists

This 1st year module forms part of the Computer Science year-long placement programme. This non-credit bearing module provides you with both the opportunity and support to develop the employability and personal development skills needed to explore career opportunities and source and apply for a professional placement.

Delivery is through a combination of lectures and workshops, predominantly in term 2. Completion of all sessions is strongly advised.

This is a non-credit-bearing module that aims to prepare you for the tasks and activities related to deciding on what placement to undertake, finding and securing a suitable placement for the third year of your degree.

The combination of lectures and workshops focuses on career exploration, self-awareness of personal strengths, and searching for and applying for placements.

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COM2011: Machine Learning and Data Science

This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows, alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation.

This module is suitable for students with sufficient preparation in Mathematics and Programming.

This module aims to equip you with the fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising complex datasets.

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COM2017: Introduction to Prompt Engineering

This course introduces you to the emerging field of prompt engineering, which involves designing and refining prompts to effectively utilize AI and language models, such as chatGPT. No prior computing knowledge is required. The course will cover fundamental concepts, practical applications, and ethical considerations, providing students with the skills to craft effective prompts for various AI tools. It requires no prior knowledge of computing or of language models.

• Understand the basics of AI and language models, and the metaphors used to understand these.
• Learn the principles of prompt engineering, and develop the skills to create and refine prompts for a range of applications.
• Explore the social, legal and ethical implications of language models.
• Gain practical experience through hands-on projects and exercises.

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COM2020: Team Project

This module gives you the opportunity to work collaboratively on a substantial practical problem, which you will solve from a computational and data-based perspective. Teams will apply technical skills in software development and data analysis while managing project planning, teamwork, and communication. During the module you will design and develop a solution that balances innovation with feasibility, and develop a prototype that you demonstrate tackles the project according to specific measures of success. You will develop the professional skills required to succeed in technology and data-driven industries.

The aim of this module is to equip you with the necessary practical and theoretical skills to enable you to develop and implement a computational and data-driven solution to a given problem. Early in the module you will be presented with a realistic problem, and you will be asked to work within a team to propose, develop, and implement a solution to the problem. You will learn how to apply a range of evaluation measures to evaluate the success of your team’s solution. Throughout the module you will learn about and deploy teamwork skills to ensure the success of your project.

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ECM2414: Software Development

The module will introduce you to software design and development concepts and methods, alongside intermediate and advanced constructs and concepts in the Java programming language, and the programming paradigms these relate to. This includes generic programming (and Java generics), concurrent programming (via Java threads), design patterns, networked programs and nested inner classes. We will also cover widespread tools in software development, including version control and unit testing.

This module will introduce you to methods for the rigorous testing and assessment of software, and prepare you for complex programming tasks in a specific object-oriented programming language, including advanced concepts and syntax, and the use of multiple programs in parallel.

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ECM2419: Database Theory and Design

This module will give you an insight into the theoretical and technical issues underlying current and future database management systems. You will acquire practical and theoretical competence in database modelling and design, as well as gaining familiarity with modern state-of-the-art database technology.

Prerequisite module: ECM1400, ECM1410, ECM1413 or equivalent.

The intention of the module is to equip you with the theoretical and practical knowledge needed to design, develop and manage database systems using modern database management systems. You will get hands-on experience on a selected database management system that is currently in commercial use. By the end of the module you will be competent with the methods for designing, developing and managing database systems and their associated forms-based applications.

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ECM2423: Artificial Intelligence and Applications

Artificial Intelligence is the science of getting computers to do things which, when done by humans, involve the exercise of intelligence. It has been an important strand of Computer Science throughout the lifetime of that discipline, and has exerted a significant influence on other areas of Computer Science as well as on practical applications. This module will provide you with a broad overview of Artificial Intelligence, as well as a more detailed understanding, both practical and theoretical, of selected topics within this area. This module is suitable for any student who has a basic knowledge of computer programming, as well as linear algebra, discrete mathematics, and probability theory.

Pre-requisites: ECM1415 and ECM2418

In this module we aim to provide you with a general introduction to some of the main topics within the broad field of Artificial Intelligence, beginning with an overview of the history and philosophy of AI, then proceeding to a more detailed examination of a range of specific sub-areas, including logic and knowledge representation, searching algorithms, machine learning, and natural language processing.

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COM2014: Computational Intelligence

Computational intelligence is the science of computational systems that are able to perform specific tasks, adapting to particular data. The module will equip you to design and use computational intelligence to solve a variety of problems such as planning, scheduling, optimisation, using a variety of techniques including biologically inspired computational, fuzzy logic, agent-based models and simulation.

Pre-requisite Modules: COM2013 (Data Science Group Project 2); ECM1400; MTH1004

The aim of this module is to introduce and give you practice in some of the main areas of computational intelligence that can be used to solve problems arising in data science. It aims to give you and understanding of the theoretical basis of these methods and their relation to other artificial intelligence techniques. Specifically, it will introduce classical “crisp” logic and knowledge representation before proceeding to fuzzy logic to cope with uncertain and vague processes. Searching and optimisation arise in many contexts and this module aims to introduce you deterministic and stochastic optimisation methods, particularly evolutionary optimisation.

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COM2019: Programming for Prompt Engineering

Prompt engineering is the art and science of interacting with large language models. These models are increasingly important in computer science and is being rolled out into applications. In this module you will program in Python to access both public large language modules, such as GPT-4, and local language models. To undertake this module you need to have some experience of using language models (through a chat interface) and of basic programming.

This model aims to give you skills to programmatically access the contents of large language models, using the Python language. This will allow you to batch process texts and/or images to undertake tasks across a range of applications. These might include, for example, sentiment analysis, text/image classification, text summarisation. It also allows you to undertake studies of language models by probing their behaviours in an experimental manner.hat do lecturers hope to cover in this module in terms of knowledge and learning opportunities for the students? Include details of research-enriched learning/ teaching and links to employment.

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ECM2400: Employability and Placement Preparation for Computer Scientists

This module provides you with key employability skills that will prepare you to apply and secure a placement or internship. You will be shown the skills required to get through the application process as well as those soft and business skills employers will be expecting. The onus is on you to secure a placement or internship but there is plenty of support from the Student Experience and Employability Team, and Career Zone to assist you. The module is timetabled predominantly for the Autumn Term when most employers are recruiting for placements and internships, with some sessions in the Spring term for support if you are making speculative approaches to SME’s or haven’t secured work experience via the standard recruitment timeline.

This module is a pre-requisite for those students looking to secure a placement on the Industrial Placement Programme. If undertaking the ECM3419 or EMP3001 modules then please note that all paperwork must be submitted and approved by the module leader in advance of placement start date.

You can undertake your work placement in the UK, any of the European Union countries that participate in the Erasmus+ programme or other approved international setting.

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SPA2009: Data Science in Society

This module will focus on the societal context for data science, machine learning and artificial intelligence. An increasing number of social, governmental and commercial processes now take place in online or digital environments, making it important to consider the ways in which data is used to make decisions and how the application of computational methods to engineer social processes can be managed in ways that are ethical, transparent and socially acceptable. This module will teach you the core knowledge around data ethics, privacy, fairness and data governance. You will be encouraged to form your own opinions on how digital tools can best be developed to deliver benefits and avoid harm. Seminar discussions and ethical case studies will be used to highlight and explore different social issues around data science.

Suitable for non-specialists and interdisciplinary pathways.

This module aims to:

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ECM3419: Industrial Placement

This module will provide you with extensive practical work experience in a business or commercial setting that is of direct relevance to your development as an experienced computer scientist. You will apply the knowledge and skills from taught modules to a real problem in computer science at a professional level. You will be encouraged to use imagination and creativity in problem solving and to develop communication skills, planning and time management and team-working skills. Placements will involve a substantial technical role in the host organisation. Individual placements are subject to availability and approval by the module leader.

This module aims to provide students with the experience of working in industry in order for them to apply the knowledge and skills acquired in an academic environment to an industrial setting.

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ECM3401: Individual Literature Review and Project

This is the module in which everything you have learnt in your Computer Science studies comes together in a substantial piece of individual project work, involving initial research and literature review, and specification and design of a software system, followed by implementation, testing, evaluation, and demonstration of the system. You will work under the supervision of an individual staff member who will provide guidance and advice as appropriate.

The aim of the module is to enable you to consolidate the knowledge, understanding, techniques and skills acquired over the previous two years through the specification, design, implementation, testing, evaluation and demonstration of a software system. The module includes both initial research into the project area (including production of a literature review) and production of the system itself following an appropriate development method.

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COM3021: Data Science at Scale

Data science relies on large amounts of data to be effective and many commercial and scientific applications require the analysis of large quantities of heterogenous, noisy data on distributed machines. This module will examine the ways in which algorithms for data science can be implemented for large data and will discuss new algorithms specifically designed for large scale data. You will also work with large-scale distributed and cloud systems for storing and computing with big data.

Through theory and practice this module aims to equip you with an understanding of the principles of distributed computing, particularly on cloud-based systems, the ways in which data can be stored and accessed to allow efficient computation, and efficient algorithms for large-scale computation.

Distributed cloud computing will provide you with the underpinning knowledge required to develop and implement machine learning and artificial intelligence algorithms on distributed high-performance computing systems.

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COM3024: Computer Vision

How do we recognise objects and people? How can we catch a ball or navigate a busy room without collisions? These everyday tasks have challenged AI scientists for decades. Recent advances in computer vision have led to major improvements in applications such as face detection, body tracking, autonomous vehicles, and action recognition.

This module introduces the fundamentals of computer vision, covering both classical and state-of-the-art methods. You will gain a theoretical understanding of key algorithms, along with practical skills in image processing, feature extraction, object detection, segmentation, and deep learning for vision tasks. The course also explores 3D vision and modern topics such as video analysis and low-shot learning, providing a broad foundation for solving real-world vision problems.

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COM3029: Social Networks and Text Analysis

The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.

The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open source packages.

The module will be taught in lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material. Assessments will include assessed pitch-deck presentation of the mini-project and an individual mini-project involving the applications of social network and text analysis.

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COM3031: Probabilistic Machine Learning

This module provides an advanced exploration of machine learning and artificial intelligence, focusing on probabilistic modeling, inference techniques, and structured learning methods. It also examines key theoretical foundations alongside advanced techniques, such as Bayesian Neural Networks and Variational Autoencoders, which enable uncertainty quantification and probabilistic generative modeling.. The module delves into Bayesian theory, its role in handling uncertainty, and its connections to approximate inference methods and information theory. Students will also explore techniques for modeling temporally and spatially structured data, including Hidden Markov Models. Additionally, the module introduces reinforcement learning. By integrating probabilistic reasoning, approximate inference, and structured learning, this module equips students with the theoretical depth and practical skills required for tackling complex machine learning problems.

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COM3032: Foundations of Human-Centred AI

You will study foundational concepts in how to use Artificial Intelligence in software systems that that interact with humans. This will involve learning about human psychology including computational theories of how people represent and process knowledge and how we learn and work together. You will learn about topics including, how people make decisions, how they perform perceptual/manual tasks, how human vision works. You will use these theories to build and critically evaluate Artificial Intelligence systems that work with people.

You will attend a weekly class in which an expert in Human-centred AI will lead discussions about a particular topic. You will work individually and in groups to investigate assigned topics and present your work.

Some mathematics and Python knowledge is needed for this module. No prior knowledge of human psychology is required

The module is recommended for interdisciplinary pathways.

Please note this module has co-requisites of COM3028 and ECM3420, or ECM3401.

The module will cover topics such as recommender systems, emotion detection systems and decision support systems. It will also cover topics including how to model humans with computer programs using techniques such as deep reinforcement learning, Bayesian inference, optimisation and game theory.

The module will be informed by the latest research in Artificial Intelligence and Human-Computer Interaction.

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ECM3412: Nature-Inspired Computation

There is a wide range of tasks, including product design, decision-making, logistics and scheduling, pattern recognition and problem solving, which traditional computation finds either difficult or impossible to perform. However, nature has proven to be highly adept at solving problems, making it possible to take inspiration from these methods and to create computing techniques based on natural systems. This module will provide you with the knowledge to create and apply techniques based on evolution, the intelligence of swarms of insects and flocks of animals, and the way the human brain is thought to process information. This module is appropriate for any student with an interest in natural systems, optimisation and data analysis who has some programming and mathematical experience.

This module aims to provide you with the necessary expertise to create, experiment with and analyse modern nature-inspired algorithms and techniques as applied to problems in industry and industrially motivated research fields such as operations research.

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ECM3422: Computability and Complexity

It is popularly supposed that there is no limit to the power of computers to perform any task, so long as it is sufficiently well defined, and to do so quickly and efficiently. In fact this is not so, and it can be proved mathematically that there are well-defined computational tasks which cannot, in principle, be performed by computers as we know them; and other tasks which, while they can be performed, cannot be completed in a feasible amount of time. This module will introduce you to the Turing Machine model of computation which underpins the fundamental theories of computability (concerned with what can be computed at all) and complexity (concerned with how efficiently things which can be computed can be computed). These theories will be introduced in a precise and formal way, and the main results and theorems will be stated and proven.

The overall aim of the module is to introduce the mathematical basis and practical implications of the classical theory of computability and complexity and to consider the extent to which this is still relevant to modern developments in computing.

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ECM3428: Algorithms that Changed the World

Algorithms are precisely defined procedures designed to solve computational tasks: they are the life-blood of computing. This module is designed to highlight the importance of algorithms in Computer Science, providing you with an understanding of what algorithms are, how they can be specified and evaluated, and what they can be used for. These general ideas will be illustrated throughout by means of an in-depth study of a range of example algorithms which have played an important part in the development of Computer Science and underpin current computing practice. The prerequisite knowledge may be obtained from two first-year computer science and mathematics modules.

PRE-REQUISITE MODULES: ECM1400, ECM1414, ECM1416

In this module, you will build on the knowledge acquired in ECM1414 (Data Structures and Algorithms) with a more systematic exploration of a range of different types of algorithms and the principles of their design and analysis. A range of specific computational problems will be covered (e.g., operations on strings, graphs, and other data structures, numerical problems), and different algorithms for these problems analysed.

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ECM3446: High-Performance Computing

The demand for ever-increasing computational power drives the development and exploitation of high-performance computing that underpins leading edge research in computationally intensive scientific and engineering fields. This module is designed to equip you with a solid foundation and useful skills in high-performance computing. In this module you will learn about current high-performance computer architectures and how the computer architecture influences the performance of algorithms and programs. You will also develop skills in parallel algorithm design and parallel programming, and will gain experience of using a high-performance computing system.

PRE-REQUISITE MODULES : ECM1416, ECM2433

This module aims to provide you with a thorough grounding in parallel programming and the architectures used in high-performance computing. After presenting the fundamental ideas and basic concepts of high-performance computing, the module outlines the architectures, components and parallel programming of high-performance computers. The module will introduce you to recent developments and future trends in architecture and algorithms in high-performance computing.

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