Computer Science with Artificial Intelligence MSci/BSc (Hons)

Get an insight into algorithms, data structures, advanced programming techniques and hard problems, covering complexity and computability as well as key aspects of the theory of computer science. Operations on complex data structures, such as trees and graphs, will be introduced and developed within a practical context.

This module will introduce you to various algorithmic and heuristic techniques used to tackle hard problems. You will also be introduced to the theory behind multi-threaded programming, enabling you to design and implement concurrent applications. Explore the limitations of computation, the formal specification of languages and the major models of computation, gaining an understanding of what these tell us about issues of computability and complexity.

Compulsory

This module brings together data science, artificial intelligence and machine learning. Data science uses a range of methods to extract insights from data. Artificial intelligence (AI) is dedicated to how computer systems can conduct work otherwise requiring human intelligence to learn, reason and make decisions.

Explore the essentials in each area, including machine learning (ML), as well as the relationships between data science, ML and AI. Uncover the processes and techniques that underlie modern real-world systems such as the prediction of customer behaviour, medical diagnosis and smart vehicles.

Compulsory

Through hands-on labs and real-world projects, you will study modern operating systems, networks and associated security. Explore the structure and components of operating systems, including advanced OS services, and functions, such as process control, file management, threading, memory management and device access. You will evaluate emerging trends and technologies alongside the fundamentals of protection and security and learn how to design and implement secure architectures and operating systems.

Compulsory

Explore the practical applications of artificial intelligence (AI) across various industries. Get hands-on experience with AI tools and techniques, understand the ethical implications of AI and develop skills to implement AI solutions in real-world scenarios.

Covering key AI concepts and a broad overview of its applications, this module aims to teach machine learning, exploring supervised and unsupervised learning, neural networks and deep learning, with practical exercises to reinforce these concepts. It also examines natural language processing (NLP), where you learn about text analysis, sentiment analysis and language models (including large language models like Copilot and ChatGPT).

Computer vision is also covered, involving image processing, recognition and convolutional neural networks (CNNs), with practical tasks to apply these techniques. Examine robotics and autonomous systems, considering the fundamentals of robotics and the role of AI in autonomous vehicles.

Ethical considerations in AI, including bias, fairness and regulatory frameworks, are examined to understand the societal impacts of AI technologies. This module concludes with case studies of AI applications in various industries such as healthcare, finance and manufacturing.

Compulsory

Learn advanced programming paradigms, techniques and approaches to improve efficiency and address challenging real-world computational problems. This module covers advanced programming paradigms, parallel and distributed programming, and GPU programming.

This module focuses on functional programming, solving problems exclusively in this paradigm using a relevant language such as Haskell. Module content considers logic programming and the emerging technology of SAT/SMT-solvers with their sophisticated search heuristics and portfolio of specialist solvers.

Explore GPU programming fundamentals, including GPU architecture, CUDA and OpenCL. By the end of this module, you should learn how to write and optimise GPU kernels, manage memory and compare GPU and CPU performance. Real-world parallelised tasks such as scientific simulations, image and signal processing and machine learning will be discussed.

Compulsory

• Natural Language Processing and Generative AI – 30 credits
  

  Explore natural language processing (NLP) and generative artificial intelligence (AI). This module covers the theoretical foundations and practical applications of NLP and generative models, providing you with hands-on experience with modern tools and techniques.

  The module begins with an introduction to NLP, covering text preprocessing, tokenisation and basic tasks like POS tagging and named entity recognition. You will then explore language models and transformers, followed by text classification and sentiment analysis projects.

  Explore generative models such as GANs, VAEs and text generation with GPT. Ethical considerations and societal impacts of NLP and generative AI are examined through case studies and discussions on bias and fairness. This module concludes with advanced topics and industry applications.

• Neural Networks and Deep Learning – 30 credits
  Deep learning is a part of machine learning that uses layered neural networks to understand complex patterns in data. Learn the fundamentals of neural networks and deep learning and build practical skills in designing, testing and evaluating neural network models. Explore more advanced deep learning topics and techniques, such as convolutional neural networks, recurrent neural networks and generative models. This module also looks at how deep learning is used in real-world applications including computer vision, language processing and reinforcement learning.

Availability of optional modules may vary depending on student demand.

Compulsory

This final year project module gives you an extended opportunity across two blocks to design and complete a major project that brings together everything you have learned during your course. It’s your chance to showcase your knowledge and skills through a piece of work you can be proud of and share with both academic audiences and potential employers. The module includes a comprehensive teaching programme that supports you in developing a wide range of essential research and professional skills.

Learning covers how to propose and plan your research effectively, considering scope, ethics and value. Explore how to innovate through research, position your work within current knowledge by understanding what experts in the field have done, and demonstrate your contribution to that knowledge.

You should gain a solid foundation in key research and innovation methods and learn how to present and defend your work with confidence, both in writing and in discussion. You should develop strong project management skills such as managing risk, setting goals, meeting deadlines and making the most of supervision. This module also supports your growth as an independent researcher and helps to strengthen the professional skills you will need for your future career.

Compulsory

Learn and apply advanced software development techniques informed by leading industrial practices. This module provides a comprehensive overview of advanced techniques, methodologies, and tools used in modern software development. Through a group project, gain practical experience in applying these techniques to develop complex, high-performance, and scalable software systems, adhering to professional standards.

Compulsory

Explores how machine learning is used in fields such as healthcare, agriculture, bioinformatics, image recognition and natural language processing. Develop skills to address real-world challenges involving big data. Learn about various machine learning models, including supervised learning, unsupervised learning, reinforcement learning and neural networks.

Compulsory

Gain an understanding of the principles of neural information processing, neural network models and algorithms, soft computing techniques using fuzzy logic, and the genetic fuzzy based hybrid. Learn the mathematical theory behind each model, write code to implement them, and apply these models to complex datasets. Learnings focus on application-oriented models and gain an understanding of neural AI methods to develop new algorithms when needed, rather than just using off-the-shelf tools.

Compulsory

This module focuses on decision-making in AI systems and Generative AI, along with the implications for human factors. Gain an understanding of various approaches and algorithmic tools available for making decisions in both simple and complex environments. Gain hands-on experience with techniques and tools used to create generative systems such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Transformer-based models.⁴

Compulsory