The module aims to provide students with information on Authentication, Authorisation, and Accountability (AAA) and its building blocks. An emphasis will be given on authorisation, where access control models, policies and mechanisms will be examined.

Students will review main categories of existing cryptosystems (e.g. symmetric, asymmetric) in order to understand their use and offered security properties (e.g. confidentiality, integrity, non-repudiation) in practice. They will explore operating systems security and network security concepts in connection to AAA and cryptosystems, as well as being introduced to formal verification and how it can be used to verify properties on cyber security systems.

This module provides broader exposure to alternative programming language paradigms beyond imperative and object-oriented programming. Particular emphasis is given to functional programming languages, and their unique constraints and features. More specifically, students will investigate how introducing the concept of absolute immutability into programming languages enables a suite of expressive mechanisms within programming languages including pure functions, lambdas, higher order functions, pattern matching, currying, map/reduce, and pattern matching.

As a part of this module, students will also explore why functional languages bring about increased reliability and scalability, and how they are now experiencing a resurgence within the software industry. Finally, through hands-on laboratory sessions, students 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

In this module, students will build upon the foundations of algorithms and their complexity to develop a deeper understanding of algorithmic approaches to computational problem solving. They will explore computational complexity theory, which allows us to consider the very nature of computability – including non-deterministic polynomial (NP) complexity classes such as NP-hard, NP-complete and the classes of problems which cannot be solved. Students will be introduced to classical approaches to problem solving such as divide and conquer, recursion, and parallel approaches, emphasizing their relative benefits and weakness to different classes of problem. They will study advanced data structures in depth, such as tries, heaps, suffix arrays, k-d trees, and distributed hash tables, and explore the approaches for their efficient construction and use.

These theoretical aspects are grounded through practical work in the lab and placed in the context of case studies of extreme scale and embarrassingly parallel computing, derived from real-world problem domains introduced by invited speakers where possible. Finally, students explore key implications of algorithm performance including their impact on energy efficiency and sustainability to provide a coherent interface with other modules.

This module explores some of the practical and applied aspects of cyber security: Penetration Testing and Forensics. Students will learn common approaches and tools that attackers use to undermine the security of digital systems and gain first-hand experience of the weaknesses that can be present in real-world systems through guided work in highly controlled, small-group practical labs. They will explore ways in which these attacks can be identified, how the digital traces of an attack be captured, appropriately evidenced and then interpreted at a later date. The module will wrap the technical and theoretical aspects within the legal, regulatory and ethical frameworks for the appropriate application of ethical penetration testing.

This module introduces the key ideas and fundamental principles of artificial intelligence (AI) and the types of problems that can be addressed by AI. Students will be introduced to the core concepts and philosophy of AI, including its history and definitions, classify the various approaches to AI, and discuss its presence in the modern world alongside its ethical considerations. They will unearth the underlying principles of search spaces, knowledge representation, and inference logic that form the core of rule-based systems.

Students will then go on to learn the principles of machine learning, emphasising clustering (e.g. k-means), classification (e.g. k-nearest neighbour) algorithms, linear regression, and neural networks. This deep dive provides the essential grounding necessary to progress to modules in topics such as Machine Learning, Computer Vision, and Natural Language Processing.

Computer architecture has now reached a critical juncture where we are witnessing a step change in computer performance – not due to the increased performance of individual processors, but through the inclusion of many, sometimes even thousands, of processor cores in a single computer.

In this module, students will learn how to classify the different designs of multi-processor computers such as symmetric CPUs and general-purpose GPUs. They will investigate their benefits and drawbacks and study the theories and factors that can all too easily bound their seemingly limitless computational potential. Through a combination of lab exercises and lectures, students will discover how to use contemporary software tools and techniques to create high performance applications that exploit multi-threaded instruction parallelism whilst avoiding race conditions, deadlock and livelock, and utilise GPUs to exploit data parallelism.

Extended reality (XR) refers to the interactive technologies that blend the virtual and physical worlds together into a hybrid environment or an immersive experience. The technology is based on multi-modal platforms that integrate use of ubiquitous, pervasive, wearable, and omnipresent computing.

This module situates XR's different offerings within Milgram’s continuum and identifies the needs and means of augmenting the human sensory channels. The computing perspective takes an applied approach to design, implementation, deployment, and evaluation of systems that are used to create an XR environment and deliver an immersive experience. Students will learn about the latest trends in research, emerging technologies, and novel tools, with an analytical focus on the technology and the socio-ethical implications of widespread prevalence of the technology.

The internet and the world wide web have now pervaded every aspect of our lives, from e-commerce 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 ubiquitous.

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

This module provides a deep dive into the theory and practical application of advanced operating systems (OS) and associated hardware concepts. Through a combination of lab exercises and lectures, students will investigate the ways that modern operating systems are optimized to extract the maximum performance and efficiency from 21st century computer hardware.

Students will also study how the fundamental concept of virtualisation enables safe, efficient, and fair sharing of memory and processor resources across multiple applications and services. They will investigate the structure, operation, and scalability of OS subsystems, such as memory allocators and file systems, as well as discuss the performance implications of operating systems and discover how performance can be maintained even in the presence of relatively low performance input/output. Finally, students will explore how symmetric multi-processors can be used transparently to optimize the performance of a computer, the implications this has for system software, and how and why the effective application of caching policies and temporal/spatial locality greatly affect the performance of a system.

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 with respect to economic cost, engineering effort, and environmental impact.

In this module, students will explore key concepts associated with creating sustainable computing, spanning from how a processor uses electricity to how computers shape a greener economy and society. They 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.

Distributed Systems form the foundation upon which modern community platforms such as Distributed Cloud Infrastructures, and service-oriented architectures are based (also known as “as a service” operations). Students will investigate advanced cryptography techniques used to build such systems, and security infrastructures built into the distributed systems themselves.

Students will also study the alternative design approaches to the construction of secure distributed systems and their subsequent security evaluation. More specifically, they will investigate the common vulnerabilities and attack surfaces associated with distributed systems, and the widely adopted design patterns used to mitigate them.

Computer networks have experienced an exponential growth in traffic volume and size since the early days of the Internet. Packet network technologies underpin every aspect of our daily work, social life, and entertainment – even enabling the global populous to continue working during a global pandemic.

This module investigates the evolution of network technologies to cope with the global Internet growth trends and is organized in three topic areas. Core topics explore the architecture of devices and protocols that facilitate end-to-end connectivity across the global Internet and allow control of connectivity properties, like bandwidth and latency. Research and Industry topics explore cutting-edge research and industry perspectives on the challenges that face production network technologies, such as performance and security, and elaborate on future directions in networking to address them. Finally, practical topics will introduce students to network emulation and simulation technologies and offer the opportunity to recreate realistic network testbeds. Through small group practical sessions, students will gain experience using open-source software frameworks to implement, configure and test common network functionalities, such as routing and firewalling.

Computer graphics is an interdisciplinary field which deals with visual and image aspects of computing. It underpins the development of video games, use of computer-generated imagery in movies and has helped advance machine learning, cryptography, and parallel computing.

In this module, students explore the fundamental concepts related to visual content generation through relevant theory, such as the essential mathematics, graphics data structures and algorithms, kinematics, collisions, colour, and light. In particular, students will investigate the practical aspects of graphical scenes and rendering including virtual cameras, materials, mesh manipulation, scene-graphs, animation and modelling. They will learn about hardware-specific concepts designed to improve the quality and performance of graphics applications, such as GPU programming, mobile and cloud render-pipelines, shaders, stereoscopic and volumetric rendering. Emerging technologies and trends in research are also introduced with an analytical lens to identify future challenges, opportunities, and solutions.

In this module, students will explore how to teach Computer Science (CS) as a discipline and organise the engagement activities that are contributing to addressing the digital skills gap, and inspiring new computer scientists. Through practical sessions, they will build a foundational understanding of computing pedagogy, learning to recognise how pupils study computer science and arrange teaching to respond to their needs.

This module will explore the instruments and methods for conducting effective teaching practices in a variety of settings - exploring UK and global contexts, and the differences within primary, secondary, and higher education. It aims to highlight the importance of equality, diversity, and inclusion (EDI), ethics, safeguarding and integrity considerations in CS education. Among the teaching practices, students will also learn how to plan and conduct teaching or outreach activities in schools – providing the opportunity for them to practice educational skills by contributing to activities in regional schools and supporting the development of digital capabilities of young people in Lancashire.

Computer vision is a branch of artificial intelligence, in which we aim to develop computer-based systems that can interpret and draw meaningful deductions from digital images.

This module covers the fundamentals to understanding image formation and information relating to the human visual system and some fundamental image interpretation methodologies, including convolution, edge detection and feature extraction and comparison. Students will tackle key problems in current research, including semantic segmentation, object detection, and three-dimensional image interpretation. They will cover a range of approaches, from low-level image processing to convolutional neural networks. At the end of the module, students 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.

This module aims to equip students with the necessary knowledge to efficiently gather and process the increasingly large amount of security data generated by applications and users of computer systems. Students will review the different data types, data storage and access techniques, and problems with handling massive amounts of data that is typically associated with the monitoring and auditing of cyber security systems. They will learn how to use and develop tools to address the complexities of real-time analytics that are necessary to inform critical decisions in systems administration.

Students will also learn and practice exploratory data analysis, data collection and data mining techniques to capture data with security significance and discover how to produce graphical representations of security data. This module also covers the fundamental and advanced techniques of security data visualization to enable the extraction and effective communication of insights and incidents, and enables students to develop interactive dashboards to enhance monitoring and observability capabilities.

This module will explore machine learning, which sits within the field of artificial intelligence and enables a computer to learn how to perform a task from data rather than traditional programming.

Students will study the key ideas and techniques of machine learning, which will help students to develop practical skills in problem solving and to understand the implications and potential of machine learning in business and society. They will begin by looking at real-world machine learning problems, challenges, and fundamental techniques in current machine learning methodology. Building on this, the module 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.

Digital Health concerns the utilisation of digital technologies for health and care. It has a key and ever-growing role to play in improving health systems and public health, as well as increasing and improving the equity of access to health services. It has the potential to transform health and care delivery and support individuals to improve their health.

In this module, students will discover the practical applications, implications, and enabling technologies of digital health. They will survey the sensor technologies that permit remote and automated patient monitoring, study the technologies and processes that enable patient-driven healthcare. This module also investigates the structure of health data in electronic health records, and methods for the evaluation of digital health solutions. Alongside these applied topics, students will also learn about data governance and the ethical issues surrounding digital health technologies, policy, and regulation.

This module exposes students to the challenges associated with developing firmware for embedded systems, which are increasingly common in everyday appliances with the rise of Cyber Physical Systems, such as smart cities and the internet of things.

Students will take a deep dive into embedded systems hardware and low-level programming. They will study the architecture of microcontrollers – the highly specialized, resource constrained computer processors that power embedded systems. Building on this, they will then learn about the state-of-the-art software development processes that allow us to write highly efficient code for these devices. They will discover the industry standard protocols and techniques for integrating peripherals with microcontrollers, and low power wireless network communication technologies that enable their interconnection. The module is anchored by giving students real experience with a variety of embedded systems in small group practical sessions.

All programming languages are based on theoretical principles of formal language theory. In this module, students take a deep dive into formal languages representation and grammars, and how relate to programming language compilers and interpreters.

Students will study formal language syntax and semantics, phrase structure grammars and the Chomsky Hierarchy. They will learn how to classify languages and explore the concepts of ambiguity in Context Free grammars and its implications. In particular, they will learn about the compilation process including lexical analysis and syntactic analysis, recursive descent parsers, and semantic analysis. Finally, students get to investigate the synthesis phase, where intermediate representations, target languages, and structures lead to code generation. In the School, we blend lectures with small group lab sessions where students gain hands-on experience of applying such theory.

This module provides a broad introduction to Natural Language Processing (NLP), a branch of Artificial Intelligence where we develop computational methods to analyse and understand human languages.

Students will be exposed to the core concepts of 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). They will experiment with and comparatively evaluate different methods and techniques, including rule based, probabilistic, machine learning and deep learning approaches. Students 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 are also emphasised throughout the learning and teaching in this module.

The rapid increase in consumption and innovation within Artificial Intelligence (AI) and Machine Learning (ML) has significant repercussions for cyber security. This encompasses both how AI and ML can be leveraged to augment and improve established cyber security techniques (from firewalls, risk analysis, and attack detection), as well as the emerging threat of attacks against AI itself (data poisoning, extraction, membership inference).

In this module, students will learn key concepts of secure AI, how it is being used to revolutionise the established cyber security field, as well as the emergent threats of attacks against ML models and data. By the end of the module, students will be able to compare and contrast the roles that Artificial intelligence and Machine Learning can play within the field of cyber security, analyse contemporary security threats against Artificial Intelligence and Machine Learning technologies, as well as evaluate the effectiveness of cyber security AI technologies.

This module discusses the security threats to Cyber-Physical Systems (CPS) - such as Industrial Control Systems, IoT, Smart Cities, and Connected Vehicles, and techniques to mitigate these threats. Students will learn how to identify appropriate security techniques and protocols to use depending on the specifics of a CPS. This involves understanding how to write secure applications for CPSs and alternative technologies, such as Transport Layer Security (TLS).

Students will also explore how the limitations of these systems impact the security guarantees that can be provided. In addition to security, this module will examine the safety and privacy threats CPSs will be subject to and explore the interconnectivity between them and security. By the end of the module, students will be able to design experiments to test the effectiveness of a CPS’s security, as well as translate their experiences of securing one CPS to another within a different domain.

The Fourth Year Project will focus on a significant specification, design, implementation and/or evaluation project at the suitable level for an MSci qualification. The project sees students tackle a real-world problem by applying their knowledge in computer science. The project is usually achieved in conjunction with an industry placement; however, it can be completed at the University. Suggestions made by industry will be vetted by a team of academics to ensure appropriate depth, and if no suitable project with industry can be found, one will be provided by academic staff.

Weekly guidance is given from a member of academic staff from the department to ensure the necessary level of academic content and rigour is being maintained. There are also Business Development mentors in the Knowledge Business Centre (KBC) to provide students with an insight into the day-to-day expectations and responsibilities of working with industry. The Fourth Year Project is designed to challenge students and develop their existing knowledge, understanding and skills from their undergraduate degree to produce a significant piece of academically rigorous project work.

Students complete a 10 week industrial placement in the Lent term of their 4th year. The University has a range of businesses from SMEs to large corporates for students to be placed in. There are no taught elements in this module, but students have access to an academic supervisor to guide and assist them during the placement. Placements are assigned to students in the Michaelmas term.

Students will gain first-hand experience of working in a contemporary ICT related environment, developing an appreciation and understanding of professional practices and codes of conduct in industrial, commercial and professional settings. Companies will set tasks that are related to students’ knowledge and experience gained throughout their degree, allowing them to apply it in a professional setting. Placements are offered by a variety of companies with different topics. Through an initial matching and application process, we ensure the biggest possible overlap between student interest and company requirements.

Computer scientists frequently face problems for which there are no ready answers and that therefore require research. This module introduces students to research methodology, the ‘classic’ empirical methods of survey, case study, ethnography and experiment, and design and innovation as the context in which computer scientists apply research methods.

On a fundamental level, students will explore how empirical research involves questions of sampling, data collection, study design and data analysis, and how each of these involves trade-offs that can limit the validity of results gained and conclusions drawn. On a practical level, students will engage in a collaborative innovation project through which they practice application of methods in design and evaluation of novel interactive solutions.

Distributed Systems form the foundation upon which modern community platforms such as Distributed Cloud Infrastructures, and service-oriented architectures are based (also known as “as a service” operations). Students will investigate advanced cryptography techniques used to build such systems, and security infrastructures built into the distributed systems themselves.

Students will also study the alternative design approaches to the construction of secure distributed systems and their subsequent security evaluation. More specifically, they will investigate the common vulnerabilities and attack surfaces associated with distributed systems, and the widely adopted design patterns used to mitigate them.

Computer graphics is an interdisciplinary field which deals with visual and image aspects of computing. It underpins the development of video games, use of computer-generated imagery in movies and has helped advance machine learning, cryptography, and parallel computing.

In this module, students explore the fundamental concepts related to visual content generation through relevant theory, such as essential mathematics, graphics data structures and algorithms, kinematics, collisions, colour, and light. In particular, students will investigate the practical aspects of graphical scenes and rendering including virtual cameras, materials, mesh manipulation, scene-graphs, animation and modelling. They will learn about hardware-specific concepts designed to improve the quality and performance of graphics applications, such as GPU programming, mobile and cloud render-pipelines, shaders, stereoscopic and volumetric rendering. Emerging technologies and trends in research are also introduced with an analytical lens to identify future challenges, opportunities, and solutions.

This module will explore machine learning, which sits within the field of artificial intelligence and enables a computer to learn how to perform a task from data rather than traditional programming.

Students will study the key ideas and techniques of machine learning, which will help students to develop practical skills in problem solving and to understand the implications and potential of machine learning in business and society. They will begin by looking at real-world machine learning problems, challenges, and fundamental techniques in current machine learning methodology. Building on this, the module 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.

Digital Health concerns the utilisation of digital technologies for health and care. It has a key and ever-growing role to play in improving health systems and public health, as well as increasing and improving the equity of access to health services. It has the potential to transform health and care delivery and support individuals to improve their health.

In this module, students will discover the practical applications, implications, and enabling technologies of digital health. They will survey the sensor technologies that permit remote and automated patient monitoring, study the technologies and processes that enable patient-driven healthcare. This module also investigates the structure of health data in electronic health records, and methods for the evaluation of digital health solutions. Alongside these applied topics, students will also learn about data governance and the ethical issues surrounding digital health technologies, policy, and regulation.

Large scale distributed computing systems are now commonplace, implemented through the use of “cloud infrastructures” where computing and storage resources are pooled into data centres around the globe. In scientific terms, these are examples of the wider field of Distributed Systems.

In this module, students will learn about the fundamental principles that underpin modern distributed systems, the abstractions on which they are based, and their characteristics. Particular emphasis is placed on the scalability and fault-tolerance of these systems, and students will get to undertake a deep dive into the commonly used frameworks for distributed systems such as Google infrastructure and highly distributed peer to peer approaches. Small group practical labs reinforce theory through hands-on experience of distributed systems development.

All programming languages are based on theoretical principles of formal language theory. In this module, students take a deep dive into formal languages representation and grammars, and how relate to programming language compilers and interpreters.

Students will study formal language syntax and semantics, phrase structure grammars and the Chomsky Hierarchy. They will learn how to classify languages and explore the concepts of ambiguity in Context Free grammars and its implications. In particular, they will learn about the compilation process including lexical analysis and syntactic analysis, recursive descent parsers, and semantic analysis. Finally, students get to investigate the synthesis phase, where intermediate representations, target languages and structures and lead to code generation. In the School, we blend lectures with small group lab sessions where students gain hands-on experience of applying such theory.

This module discusses the security threats to Cyber-Physical Systems (CPS) - such as Industrial Control Systems, IoT, Smart Cities, and Connected Vehicles, and techniques to mitigate these threats. Students will learn how to identify appropriate security techniques and protocols to use depending on the specifics of a CPS. This involves understanding how to write secure applications for CPSs and alternative technologies, such as Transport Layer Security (TLS).

Students will also explore how the limitations of these systems impact the security guarantees that can be provided. In addition to security, this module will examine the safety and privacy threats CPSs will be subject to and explore the interconnectivity between them and security. By the end of the module, students will be able to design experiments to test the effectiveness of a CPS’s security, as well as translate their experiences of securing one CPS to another within a different domain.