Human-computer interaction (HCI) is concerned with all aspects of designing, building, evaluating, and studying systems that involve human interaction.

From a computing perspective, the focus is on enabling interaction through user interfaces and on creating interactive systems that provide a positive user experience. The module introduces you to the foundations of HCI, where you delve into human behaviour, technologies for interaction and human-centred design. You will review human perception, cognition and action, and relate these to design principles and guidelines. As part of this, you will discuss different examples of user interfaces and key technologies such as pointing. You will then be introduced to the practical methods for designing and evaluating, including legal, social, ethical and professional considerations in relation to people and society, such as inclusive design practices, bias, and privacy.

Statistics allows us to estimate trends and patterns in data and gives a principled way to quantify uncertainty in these estimates. The findings can lead to new insights and support decision-making in fields as diverse as cyber security, human behaviour, finance and economics, medicine, epidemiology, environmental sustainability and many more.

Dive into the behaviour of multivariate random variables and asymptotic probability theory, both of which are central to statistical inference. You will then be equipped to explore one of the most fundamental statistical models, the linear regression model, and learn how to apply general statistical inference techniques to multi-parameter statistical models. Statistical computing is embedded in the module, allowing you to investigate multivariate probability distributions, simulate random data, and implement statistical methods.

Researching, collaborating, writing and presenting are key skills for all students. Collaborating with fellow students, you will investigate a chosen mathematical or statistical subject and produce a report and presentation to share your findings. As part of this, you will learn how to format and structure scientific reports and papers, use specialised documentation software like LaTeX, conduct research, cite and reference sources.

An introduction to two essential concepts in modern computing systems, cyber security and data engineering. We explore the building blocks of the Authentication, Authorisation and Accountability (AAA) framework, including access control models, security policies and mechanisms. You will review the main categories of existing cryptosystems to understand their security properties, discuss basic concepts of systems security, study the common approaches and tools that attackers use and gain first-hand experience tackling the weaknesses that can be present in real-world systems through guided work in a highly controlled, small-group practical lab.

You will gain a practical and theoretical background in the design, implementation and use of database management systems. This will incorporate the consideration of information quality and security, Entity-Relationship Models, the relational model and the data normalisation process, and alternative schema definitions, SQL, NoSQL and Object-Oriented data models, big data, as well as transaction processing and concurrency control.

Never has the collection of data been more widespread than it is now. The extraction of information from massive, often complex and messy, datasets brings many challenges to fields such as statistics, mathematics and computing.

Develop the skills and understanding to apply modern statistical and data-science tools to gain insight from contemporary data sets. By addressing challenges from a variety of applications, such as social science, public health, industry and environmental science, you will learn how to perform and present an exploratory data analysis, deploy statistical approaches to analyse data and draw conclusions, as well as developing judgement to critically evaluate the appropriateness of chosen methods for real-world challenges.

Delve into the key principles of artificial intelligence (AI), touching on the core concepts and philosophy of AI and discussing its presence and ethical challenges in the modern world. Throughout, you will unearth the underlying principles of search spaces, knowledge representation and inference logic that form the core of rule-based systems, before learning the principles of machine learning, clustering, classification, linear regression and neural networks.

From this, you will have the grounding necessary to progress to modules in topics such as machine learning, computer vision, and NLP. You will also gain a deeper understanding of computational problem solving, exploring the very nature of computability, including non-deterministic polynomial (NP) complexity classes such as NP-hard, NP-complete and problems which cannot be solved. Be introduced to classical algorithmic approaches to problem solving including divide and conquer, recursion, and parallel approaches, exploring their relative merits for different classes of problem.

Extended reality (XR) refers to the interactive technologies that blend virtual and physical worlds into a hybrid environment or immersive experience. The technology is based on multi-modal platforms that integrate the use of wearable computing. In this module, you will explore different uses of extended reality within the Reality-Virtuality Continuum and identify the needs and means of augmenting human senses.

You will take an applied approach to the design, implementation, deployment, and evaluation of systems that are used to create an XR environment and deliver an immersive experience. To do this, you will study the latest trends in research, emerging technologies, and novel tools, with an analytical focus that assesses the socio-ethical impacts that may result from widespread usage of XR. A key topic will be the computer graphics technology that enables extended realities to exist visually, exploring the fundamental concepts related to visual content generation through relevant theory and practice using current game engines.

The internet and the world wide web have now invaded every aspect of our lives, from ecommerce and entertainment to logistics and social media. Increasingly, application software is no longer written for specific devices, but for internet web browsers. The internet has replaced operating systems as the de-facto platform for application development, making an already global phenomenon truly impactful.

This module explores the various approaches to the development of internet applications, investigating both the client and server-sides, and discussing the trade-off of performance, scalability, privacy and trust associated with these approaches. You will review the role of ‘cloud infrastructures’ (federated distributed computation) in the provision and management of internet applications. Through interactive lectures and practical sessions, you will study common frameworks for client-side application development and create and deploy an internet application from first principles.

Study at one of our approved international partner universities in your year abroad. This will help you to develop your global outlook, expand your professional network, and gain cultural and personal skills. It is also an opportunity to gain a different perspective on your major subject through studying the subject in another country.

You will choose specialist modules relating to your degree and also have the opportunity to study modules from other subjects offered by the host university.

Places at overseas partners vary each year and have previously included universities in Australia, USA, Canada, Europe, New Zealand and Asia.

You will undertake a substantial individual project, typically involving the principled design, implementation, and evaluation of a substantial piece of software, experimental study, or theoretical work. To assist in this, an academic will provide a large range of project ideas from both the School of Computing and Communications and the School of Mathematical Sciences, which you will rank by level of interest before being allocated to a supervisor. You will also have the opportunity to write your own project idea and find a supervisor that would like to support you, and projects can be carried out in collaboration with an external partner, such as a company.

Throughout the project, you will be expected to attend regular one-to-one meetings with your supervisor, who will provide guidance and feedback as you explore your topic and complete previously agreed goals.

Dive into alternative programming language paradigms, beyond imperative and object-oriented programming. Emphasis is placed on functional programming languages and their unique constraints and features, such as more expressive type systems, immutability, pure functions and side-effects, lambdas, higher order functions, currying, map/reduce and pattern matching. You will also explore why functional languages bring about increased reliability and scalability and how they are now experiencing a resurgence within the software industry. Through hands-on laboratory sessions, you will learn a functional programming language, such as Haskell, and see how functional programming concepts are being integrated in mainstream programming languages, such as Java, Python and JavaScript, to create versatile multi-paradigm programming environments.

Understanding how data evolves over time is crucial across numerous sectors, from finance and engineering to climate science. Develop the tools to analyse temporal data, detect structural changes and build predictive models.

Using changepoint detection algorithms, you will learn how to identify abrupt changes in the mean or variance of a process, or parameters in a regression model. These methods will be introduced from a foundational perspective, developing both computational and mathematical understanding. You will then learn to handle temporal dependence by studying a popular range of time-series models, using these to generate insights about the data and produce forecasts. Throughout the module, you will learn to critically evaluate and compare models, whilst applying these techniques to real-world challenges.

Learn how to teach computer science as a discipline, including organising engaging activities that address the digital skills gap, and inspiring new computer scientists. Through practical sessions, you will build a foundational understanding of computing pedagogy, learning to recognise how learners study computer science and arrange teaching to respond to their needs. You’ll explore the instruments and methods for effective teaching practices, considering UK and global contexts, and the differences within primary, secondary, and higher education.

The importance of equality, diversity, and inclusion (EDI), ethics, safeguarding and integrity considerations in education will be highlighted throughout. You will also learn how to plan or conduct teaching or outreach activities in schools and support the development of digital capabilities of young people in Lancashire.

Computer vision is a branch of artificial intelligence which aims to build computer-based systems that can interpret and draw meaning from digital images. This module digs into the fundamentals of image formation, information relating to the human visual system, and image interpretation methodologies including convolution, edge detection and feature extraction, and comparison. You will tackle key problems in current research, including semantic segmentation, object detection and three-dimensional image interpretation. You will cover a range of approaches, from low-level image processing to convolutional neural networks. At the end of the module, you will be equipped to construct software components that implement contemporary image processing and computer vision algorithms and recognise issues within computer vision in order to develop and evaluate solutions.

Digital Health explores the utilisation of digital technologies in healthcare. These technologies have an ever-growing role to play in improving health systems and public health, as well as increasing and improving access to health services. Discover the practical applications, implications, and how to enable technologies of digital health. You will survey sensor technologies that permit remote and automated patient monitoring and study the technologies and processes that enable patient-driven healthcare. You will also investigate the structure of health data in electronic health records and methods for the evaluation of digital health solutions. Alongside these applied topics, you’ll also learn about data governance and the ethical issues surrounding digital health technologies, policy, and regulation.

Distributed systems are the foundation upon which modern large-scale infrastructures are built, such as Cloud and service-oriented architectures (also known as ‘as a service’). You’ll investigate the cryptographic techniques used to build such systems, and secure distributed systems themselves.

You’ll study the design approaches to constructing a secure distributed system, including the common vulnerabilities and attack surfaces associated with distributed systems, and the widely adopted design patterns used to mitigate them. To ensure the correctness of such systems, you will be introduced to formal verification techniques covering system specification and the verification of their correctness. This is imperative for systems that form the foundation of modern infrastructures or when we require security guarantees in mission-critical scenarios. Formal languages are used to define precise system specifications, and automated verification techniques verify their correctness. The languages enable the modelling of distributed systems and algorithms, and the verification of properties to prove their correctness.

An introduction to a variety of methods that are useful for analysing environmental data, such as air temperatures, rainfall or wildfire locations. Spatial dependence is a key feature of many environmental datasets, and the Gaussian process will be introduced as a model for continuous spatial processes. You will learn about the properties of the Gaussian process and implement this model for spatial data analysis, before investigating methods for point-reference data, such as earthquake or wildfire locations.

You will also dip into natural hazard risk management, which seeks to mitigate the effects of events, such as flooding or storms, in a manner that is proportionate to the risk. You will learn basic concepts from extreme value theory, including the appropriate distributions for extremes, and how to use these as statistical models for estimating the probability of events more extreme than those in the dataset.

All programming languages are based on theoretical principles of formal language theory. In this module, you dive deep into formal languages representation and grammars, and how they relate to programming language compilers and interpreters. You will study formal language syntax and semantics, phrase structure grammars, and the Chomsky hierarchy. You will learn how to classify languages and explore the concepts of ambiguity in context-free grammar and its implications. In particular, you will learn about the compilation process including lexical analysis and syntactic analysis, recursive descent parsers and semantic analysis. Finally, you get to investigate the synthesis phase, where intermediate representations, target languages and structures lead to code generation.

Delve into machine learning, a fundamental concept in artificial intelligence that enables a computer to learn how to perform a task from data rather than traditional programming. In this module, you will study the key ideas and techniques behind machine learning and develop the practical skills needed to understand the implications and potential of machine learning in business and society. You will begin by looking at real-world problems, challenges, and fundamental techniques in current methodology. Building on this, you will cover a variety of approaches to machine learning, from decision trees to a wide range of deep neural networks, including multilayer perceptrons, convolutional neural networks, long short-term memory, autoencoder and generative adversarial networks.

Statistical methods play a crucial role in health research. This module introduces you to the key study designs used in health investigations, such as randomised controlled trials and various types of observational study.

Issues of study design will be covered from both a practical and theoretical perspective, aiming to identify the most efficient design which adheres to ethical principles and can be carried out in a feasible amount of time, or using a feasible number of patients. Various approaches to controlling for confounding will be discussed, including both design and analysis-based methods. You will also explore different types of response data, including introducing time-to-event data and the resulting challenges presented by censoring.

Real-world studies and published articles will be used to illustrate the concepts, and reference will be made to the ICH guidelines for pharmaceutical research and STROBE guidelines for epidemiological studies.

Gain a broad understanding of Natural Language Processing (NLP), a branch of artificial intelligence where computational methods are used to analyse and understand human languages. Throughout the module, you will be exposed to the core concepts surrounding the NLP pipeline, covering methods and techniques for data collection, cleaning, tokenisation, and annotation using a hierarchy of linguistic levels (e.g. morphology, syntax, and semantics). You will experiment with and comparatively evaluate different methods and techniques, including rule-based, probabilistic, machine learning and deep learning approaches. You will also learn to apply and adapt NLP pipelines and tools to real-world text mining scenarios and problems, including examples such as health and finance. Key issues such as ethical data collection, bias in language models, and employing sustainable computing methods will also be touched upon throughout.

We introduce you to quantum computing's core principles and applications, contrasting its capabilities with classical systems. You will master Dirac notation and essential linear algebra, before examining quantum mechanics' four postulates, including qubits, gates, and circuit models. You will cover fundamental algorithms, including Deutsch's algorithm (implemented via Qiskit), Simon's problem, Bernstein-Vazirani, Grover's search (with BBBV Theorem analysis), and Shor's factorisation algorithm's impact on RSA cryptography.

Quantum cryptography components address post-quantum security and QKD protocols, while quantum information theory explores superdense coding, the no-cloning theorem and teleportation. The module concludes with emerging concepts like quantum money and the Elitzur-Vaidman bomb tester. Combining theoretical foundations with practical programming exercises, you will develop a critical understanding of both quantum computing's potential and current technological limitations, preparing you for advanced study or research in this rapidly evolving field.

Artificial Intelligence (AI) is being rapidly adopted in both research and industry, via technologies such as generative AI and large language models (LLM). They are being used for a range of applications by enhancing cyber security through the detection of anomalies, identifying threats, and monitoring abnormal activities. However, AI itself is susceptible to various attacks, such as prompt injection, data leakages, jailbreaking, bypassing guardrails, model backdoors, and more.

In this module, you will learn the fundamentals of AI for security and security for AI. This encompasses both how AI can be leveraged to augment and improve established cyber security techniques (from firewalls, risk analysis, to attack detection), as well as the emerging attacks against AI itself (data poisoning, extraction, membership inference). You’ll learn how AI is being used to revolutionise the established cyber security field, the emerging threats of adversarial attacks against ML models and data, and how to mitigate those attack.

Understand security threats to cyber physical systems (CPS), such as industrial control systems, Internet of Things and connected vehicles, as well as techniques to mitigate these threats. Compared to traditional computer systems, CPS have limited resources and are typically deployed into a physical environment. This impacts the implementation of security techniques, as due to the environment they are deployed in you must consider both digital and physical attacks.

This module introduces how to identify the appropriate security techniques to use for a CPS. You will come to understand how to write secure applications for CPS and which alternative mitigations are appropriate. You will also learn how the limitations of these systems impact the guarantee of security. In addition to this, you will examine the safety and privacy threats facing CPS and explore the interconnectivity between them and security.

Building on the statistical techniques explored so far, you will gain an understanding of both the theoretical underpinnings and practical application of frequentist statistical inference. You will then be introduced to an alternative paradigm: Bayesian statistics.

The frequentist perspective views all probabilities in terms of the proportions of outcomes over repeated experimentation and has been the foundation of hypothesis testing and experimental design in years of data-driven science and research. Meanwhile, the increasingly popular Bayesian approach arises directly from Bayes theorem, avoiding hypothetical repeated sampling. As a result, Bayesian statistics is often more intuitive and easier to communicate and naturally takes all forms of uncertainty into account.

With this in mind, you will compare and contrast these two perspectives and their associated tools. You will learn to select and justify an appropriate methodology for inference and model selection, and to reason about the uncertainty in your findings within each paradigm.

Stochastic processes are fundamental to probability theory and statistics and appear in many places in both theory and practice. For example, they are used in finance to model stock prices and interest rates, in biology to model population dynamics and the spread of disease, and in physics to describe the motion of particles.

During this module, you will focus on the most basic stochastic processes and how they can be analysed, starting with the simple random walk. Based on a model of how a gambler's fortune changes over time, it questioned whether there are betting strategies that gamblers can use to guarantee a win. We will focus on Markov processes, which are natural generalisations of the simple random walk, and the most important class of stochastic processes. You will discover how to analyse Markov processes and how they are used to model queues and populations.

Statistics and machine learning share the goal of extracting patterns or trends from very large and complex datasets. These patterns are used to forecast or predict future behaviour or interpolate missing information. Learn about the similarities and differences between statistical inference and machine learning algorithms for supervised learning.

You will explore the class of generalised linear models, which is one of the most frequently used classes of supervised learning model. You will learn how to implement these models, how to interpret their output and how to check whether the model is an accurate representation of your dataset. Lastly, you will have the opportunity to see how these models can be extended to the case of the ‘large p, small n’ question. This phrase refers to the situation in which there are many more variables than there are samples, something which is now commonplace.

Computing plays a pivotal role in addressing growing energy costs, greenhouse emissions, and the climate crisis. Whilst we can use computing and its associated digital technologies to shape a greener society (as well as create more energy-efficient software and hardware), there exist important trade-offs in respect to economic cost, engineering effort, and environmental impact. Explore key concepts associated with creating sustainable computing, spanning from how a processor uses electricity to how computers shape a greener economy and society. You will study the methods to create more energy-efficient code, energy-aware device mechanisms, as well as the benefits and drawbacks of computing and digital technology with respect to its impacts upon the environment and economy.