Artificial Intelligence with Placement

Artificial Intelligence is an exciting and rapidly advancing technology.  The computer language of choice is almost exclusively Python.  Python is a powerful language with an unmatched ecosystem of software packages. This module equips the successful student with the tools they need for a life-long career in AI programming and data-driven problem solving. The student will learn the advanced language features required for solving the challenging AI problems. Material includes tools for data manipulation and preparation, AI programming software packages and visualisation tools. All instruction employs the industry standard programming tools universally used for experimentation and result dissemination.

Dive into the exhilarating world of machine learning, where cutting-edge artificial intelligence meets real-world applications. With our comprehensive program, you'll discover how computers can autonomously learn and evolve, harnessing data and active exploration to perfect their algorithms. By mastering these revolutionary techniques, you'll gain the skills to make insightful, effective decisions tailored to a wide array of practical challenges. Our in-depth exploration of key algorithms ensures you not only understand but excel in the intricacies of machine learning, positioning you at the forefront of innovation. Embrace the future - let machine learning elevate your expertise and ambition to new heights.

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.

How do robots work?

You will learn about cognitive robotics, a branch of robotics, where knowledge drives action selection, planning, and execution. The goal is to understand cognitive robot design and develop skills to implement simple tasks using various robots in our lab.

With or without prior robotics experience, you'll learn core robotics principles through hands-on activities with real mobile robots and virtual simulators. You'll learn common software environments to operate mobile robots for navigation and object manipulation. 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 be introduced to recent challenges in robotic vision and how robots can use and understand human language. You will explore the societal impact of Socially Assistive Robots, focusing on how their physical bodies enhance interactions compared to virtual agents.

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.

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.

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.

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.

The use of AI has been rapidly increasing in the past years, and AI is now everywhere. The amount of digital data available has helped AI techniques evolve into the age of deep learning and large models. For many years, AI has found many applications in cyber security such as intrusion detection and spam detection. More recently, how to make AI systems securer, privacy-friendly and trustworthy has become an important topic. 

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

Using cyber security problems and datasets as examples, the module will equip you with the necessary knowledge and hands-on skills about machine learning and deep learning algorithms, models and architectures, how to apply them in real-world applications, and how to interpret results from AI models. In addition, the module also covers the aspects of secure and trustworthy AI in terms of threat models, attacks on AI models and defences against them.

Software is everywhere, and its development should be tailored according to its requirements. This module will cover principles of software engineering following agile principles for guiding its development. You will investigate software modelling as means to understand and manage software complexity. As an integral part of software development, you will learn principles of planning, cost and time estimation, quality, and risk evaluation. You will also learn why software should be developed considering social, professional, and ethical principles, including security and privacy principles. You will work with your peers to develop or evolve a software system. You will apply the principles of agile methodology in your project, manage your team effectively, organise your code using version control, and resolve problems via issue tracking. You will identify risks for software projects and follow the principles for developing software with the highest professional standards.

We will give you the statistical and computing skills you need to succeed in the rest of the course. Even if you have limited mathematical skills or no computer programming knowledge we will guide you to a level where you can apply advanced data science methods effectively. This module includes subjects such as introductory probability where you’ll learn how likely you are to win at a game of cards; introductory statistics such as testing a hypothesis in a clinical trial; and programming skills in a language such as R, including data visualisation. We will then move on to more advanced topics such as linear regression, which allows you to make predictions using multiple variables, and skills that will increase your employability, such as report writing, production of slides using appropriate software, and presentation skills. You will also find out about the important ethical implications of working as a data scientist.

The Year in Industry Placement provides you with a structured opportunity to combine work experience or entrepreneurial activity with academic study. The Year in Industry allows you to develop and reflect on managerial and/or professional practice in real and often complex situations, and to integrate this with the study of the relevant subject(s) of your main degree programme.

Where relevant, students develop, reinforce and apply professional and/or technical expertise in an employment or entrepreneurial context. The Industry Placement requires you to document your experiences in relation to both your university studies as well as to a wide range of employability skills. To be able to undertake this module it is necessary for you to secure a placement. The placement should be appropriate to your degree and experience. The length of the placement should normally be at least 44 weeks. It must be completed between the end of Summer Term and the start of the late Summer Term the following year. The particular combination of your degree course and choice of modules together with the great variety of increasingly diverse Year in Industry situations make the 'curriculum' of the Year in Industry essentially unique.

The Year in Industry provides you with a structured opportunity to combine work experience or entrepreneurial activity with academic study. The Year in Industry Report runs in tandem with the Year in Industry experience and offers you the opportunity to reflect and document your accomplishments during your industrial practice.

The Year in Industry allows you to develop and reflect on managerial and/or professional practice in real and often complex situations, and to integrate this with the study of the relevant subject(s) of your main degree programme.

Where relevant, you will develop, reinforce and apply professional and/or technical expertise in an employment or entrepreneurial context.

The Industry Report requires you to reflect on and evidence your experiences in relation to both your university studies as well as to a wide range of employability skills.

To be able to undertake this module it is necessary for you to secure a placement.

The placement should be appropriate to your degree and experience. The length of the placement should normally be at least 44 weeks. It must be completed between the end of Summer Term and the start the late Summer Term the following year.

The particular combination of the your degree course and choice of modules together with the great variety of increasingly diverse Year in Industry situations make the “curriculum” of the Year in Industry essentially unique.