Advanced Computer Science

This module will guide you through the different stages of working on an exciting computer science project. World-leading experts in the field will support you along the way by providing supervision and monitoring of your weekly activities. With computer systems becoming more crucial in everyday life, you’ll have the opportunity to conceptualise, design, develop, and test your own large system capable of solving the identified challenges. Alternatively, you will explore and critically evaluate state of the art literature, identify relevant research methodologies to advance topics in computer science, carry out independent investigations, and make an impact in the research community. You’ll be empowered to explore different skills learned throughout your degree programme and apply them in the specific domain identified for your master’s project. Finally, you will reflect on your project journey throughout the writing of a well-organised dissertation demonstrating your individual reflection and achieved learning.

Java is one of the most popular programming languages today, with very high demand for people with advanced Java skills in the industry. By joining this module you will be able to expand your programming skills with more advanced Java techniques.

The content is designed for well-qualified computer science students entering the MSc programme regardless of their diverse academic background. You will be equipped with skills and knowledge needed to tackle complex programming challenges and build high-quality Java applications. You will learn essential topics including generics, and advanced features such as reflection, lambda expressions, and high-order programming. By the end of the course, you will be able to confidently build substantial Java applications, communicate technical solutions, and continue their development in the ever-evolving field of Java programming.

Python is rapidly becoming the most important programming language in the world. It is used for everything from Artificial Intelligence (AI) applications and web technologies to financial modelling. Python has grown to be the prescribed language for AI and machine learning applications. Assuming no programming experience, this module prepares you for the exciting world of AI and programming by giving you the fundamental programming knowledge you need in the context of the Python programming language. Following successful completion of the module you are ready for advanced programming concepts.

Object-oriented programming is one of the most fundamental programming paradigms in Computer Science. Object-oriented programming languages are widely used within the software development industry. Focusing predominantly on the Java programming language, you will start by learning the fundamentals of programming, such as variables, statements and methods; and develop your knowledge and skills with a deep understanding of object orientation in Java, including advanced concepts such as objects and classes, inheritance and encapsulation. You fill follow a course that combines lectures with practical work, aiming to combine advanced theoretical knowledge of object-oriented programming with practical skills. Your skills and knowledge will be developed and applied over a series of practical classes and practical coursework.

How do robots work? You will learn about cognitive robotics, a branch of robotics in which knowledge plays a central role in supporting action selection, planning, and execution. The goal is that you gain an understanding of what is involved in the design of a cognitive robot. You will master the knowledge and skills to produce working implementations for simple instances of tasks with a range of robots in the Cognitive Robotics lab. Whether or not you have a previous background in robotics, you will become familiar with core areas in robotics and principles of robotic programming, by engaging in interactive practical activities with real mobile robots and virtual simulators. You will be introduced to the Robot Operating System (ROS) to operate mobile robots in navigating a physical and virtual map or to manipulate simple objects. You will be exposed to different applications of the state-of-the-art in Cognitive Robotics, such as industrial robots, collaborative robots, and human-robot interaction. You will learn about recent challenges in robotic vision and how robots can use and understand human language. You will explore the societal impact of Socially Assistive Robots and their interaction with humans, emphasising why the body of a social robot plays a significant role in enhancing such interactions, compared to traditional computer-based virtual agents.

What does it mean for a computer to be creative? How can artificial intelligence be used to generate creative behaviour? How do we work around the social, ethical and philosophical issues that arise? You’ll learn about key theoretical, practical and philosophical research and issues around computational creativity and creative AI. You will explore what computational creativity is and be given examples to consider in different creative domains. You’ll gain practical experience in writing creative AI software. We’ll also tackle how to evaluate the creativity of creative computer systems and consider contextual issues around how computational creativity and creative AI can be used. Student-led seminars will allow you to develop knowledge by reading about, discussing and reflecting on current issues under debate in the area. You will identify a specific context to focus on in more depth and, in your chosen context, you will conceptualise, design, build and evaluate your own creative AI software.

Embark on a transformative journey into Natural Language Processing (NLP). By delving into the realm of NLP, you will gain a deeper understanding of the intricacies of language-based AI and learn valuable skills that are highly sought after in the tech industry. You’ll learn about how cutting-edge technology is being used in human language understanding and manipulation. Through hands-on practical explorations, you'll discover how to wield powerful NLP tools and libraries to craft artificial intelligence code capable of deciphering human-like text. You’ll find out about the power of language models, from traditional linguistics-influenced methods to state-of-the-art architectures. You’ll become adept at deploying these models critically and successfully, with the opportunity to explore a particular topic in more depth through practical project work.

Remember the dancing men and how Sherlock Holmes unravelled the cryptic message using the frequency of letters? Such mechanisms are ubiquitous in the digital age -- when you are shopping online, transacting with your bank or simply using an end-to-end secure messaging system on your phone. This module will delve into Cryptology -- the science of creating such secure mechanisms of communication and ways in which they can be used, circumvented or broken. Starting from classical systems to contemporary technological advancements of the post-quantum world, you shall explore the fundamental principles, the nuances of their implementation and applications. Through a blend of theoretical lectures and hands-on exercises of building or attacking systems from the scratch, you shall gain insights into the complexities of cryptographic algorithms, key management, digital signatures, and secure communication channels. Moreover, you shall study the broader aspects of systems security, encompassing topics such as authentication, authorisation, and accountability. You will develop a holistic understanding of cybersecurity challenges and strategies for mitigating risks in contemporary computing environments. Overall, you will not only be equipped with the technical expertise to protect digital assets but also with a broader understanding of the societal implications of cryptology and systems security.

Data mining and knowledge discovery techniques are widely used in real-world applications. Examples of high-stakes applications include analysing data to decide whether a patient should undergo a surgery or analysing data to decide whether a customer should be granted a loan or hired for a job. In this module you will learn in detail how data mining algorithms work to automatically extract knowledge from data, and why these algorithms – which are based mainly on machine learning (but also on statistics) – are so important for today’s data-driven society. You will also learn about the broader process of knowledge discovery, which includes not only the application of data mining algorithms to real-world datasets, but also how to prepare data for the subsequent application of a data mining algorithm, and how to evaluate the knowledge discovered by a data mining algorithm. This module emphasises the use of techniques that learn predictive models that can be in principle interpreted by users, as opposed to machine learning techniques that learn black-box predictive models (not directly interpretable by users).

Deep learning is an approach to machine learning developed over the past several decades that draws heavily on our knowledge of the human brain, statistics and applied mathematics. In recent years, deep learning has grown tremendously in popularity and usefulness, largely due to more powerful computers, larger datasets and techniques to train deeper networks. You will investigate modern deep neural networks starting from the fundamentals of Artificial Neural Networks (ANNs). You will start by learning what is an artificial neuron and architectures like multilayer perceptron, expanding on algorithms for training ANNs such as gradient descent and backpropagation. You will examine applications of deep learning with particular attention to coding aspects during lectures and practical activities. You will learn to use the most popular techniques to evaluate the performance of a model on real data. For this, you will be introduced to different types of deep neural networks, from convolutional networks to recursive neural networks, and autoencoders to address different problems with a particular focus on computer vision topics.

The increasing reliance on the internet, electronic devices, mobile apps and online resources greatly improve the quality of our daily lives but also escalate the volume of our digital fingerprints. This means that, nowadays, it is impossible to plan or commit an unlawful activity without leaving digital traces scattered across local or remote storage and cyber spaces. Do you want to learn digital forensic skills that contribute to the investigation of suspected wrongdoing? This module focuses on computer forensics and multimedia forensics, and introduces concepts and best practices relevant to uncover digital evidence on an investigative setting. It introduces tools and techniques for the collection, interpretation and analysis of digital evidence supporting informed reporting of findings. You will gain hands-on experience with investigative tasks and will have the opportunity to apply different tools and techniques to answer investigative questions related, for example, to ‘what’ happened, where, when, why, who and how. Upon completion of the module, you will have a theoretical and practical appreciation of what ‘digital forensics’ entail.

Have you ever dreamed of belonging to the ‘red team’ and ‘blue team’ trying to anticipate what cyber attackers might do to penetrate a network, compromise a system or shutdown a communication infrastructure with the ultimate goal of strengthening security controls? This module will equip you with a mindset to think as an attacker while planning for an ethical and legal course of actions for security assessment and penetration testing of a target (for example, a network or a system). You will learn how attackers exploit different vulnerabilities and launch attacks in practice and how to recommend proactive countermeasures on an evidence-based fashion to minimise cyber security incidents as much as possible. You will analyse and compare cyber attackers’ strategies and tactics, including technical and non-technical approaches, hacking tools and compromise phases to pre-emptively envision which controls are missing and report findings to relevant stakeholders. Upon completion of the module, you will be in a better position to identify attack vectors and act on them using a framework of legal and ethical hacking for continuous improvement of cyber security.

Quantum technologies are currently being developed that will have a significant impact on society. In this module we will study two particularly promising quantum technologies: quantum computation, and quantum cryptography. The module will begin with an overview of the mathematical and theoretical knowledge required to tackle these more specialised and advanced topics. You will then learn the basic principles of quantum algorithms and quantum cryptography. You will gain an understanding of what these technologies can and cannot do, where these technologies are heading, and what the extent of their impact on our society may be.

Machine learning is not only central to modern artificial intelligence, but across sciences and everyday life. You will learn how computers can program themselves automatically, learning from data or from active exploration of the world. You will develop ability to make sound, rational and effective judgements and decisions about the use of the core machine learning algorithms for different types of practical problems. Selected algorithms will be investigated in great depth, to give you deep understanding of the inner workings of machine learning.

Have you ever wondered what happens to a computer network when it is under cyber-attack? Do you know the different types of cyber-attacks that can be performed on computer networks? Have you thought about the stakeholders involved in cyber-attacks? Do you know how to protect your computer network from possible cyber-attacks? These questions – and many more – will be covered in this module. Various threats and countermeasures will be discussed, along with recent computer network technology trends and novel protocols. It is pretty much impossible to prevent every attack, therefore, we need to learn from past cyber-attacks (e.g., how attackers gained access, what vulnerabilities they exploited, how they moved within the network, what they gained) to be prepared to deal with them when they occur, adopting a defence-in-depth strategy. You will learn about network threats and vulnerabilities, as well as tools and techniques that can help you secure and protect your computer network.

The advance of Information and Communications Technology (ICT), such as the internet, mobile computing, IoT (Internet of Things) and AI, have blurred the boundary between the physical world and cyber space. A massive amount of digital data is now being generated, collected, stored, processed, transmitted and shared constantly across many different systems, people and organisations. In such a “big data” world, how can we protect our privacy and ensure others respect our privacy? What privacy-enhancing tools and techniques can be used and how well do they work? In addition to privacy concerns, cybercrime and other forms of online harms have increasingly become more prevalent; they are highlighted by many as major threats to all citizens and the society at large. What legal efforts have been made to protect us and what more can be done? What legal considerations do cyber security experts need to know about and how can they utilise and integrate these legal aspects in their work? This module will guide you through all such questions to grasp the latest development on privacy and cyber law and to be able to apply such knowledge in practical scenarios where privacy and legal considerations are important factors.

The use of AI has been rapidly increasing in recent years, and AI is now everywhere. The vast amount of digital data available has driven AI techniques into the age of deep learning and large models. For many years, AI has been applied in cyber security for tasks such as intrusion detection and spam detection. More recently, ensuring AI systems are secure, privacy-friendly, and trustworthy has emerged as a critical focus.

Do you want to understand how different AI architectures and models work, and how they are applied to address cyber security challenges and enhance the security of systems and computer networks? Do you want to explore how AI models and pipelines themselves can be vulnerable to attacks that compromise their performance, security, privacy, or trustworthiness?

Using cyber security problems and datasets as examples, this module equips you with the knowledge and hands-on skills required to understand machine learning and deep learning algorithms, models, and architectures. You will learn how to apply these techniques in real-world applications and interpret the results from AI models. Additionally, the module covers secure and trustworthy AI, including threat models, attacks on AI systems, and the defences against them.