ARTIFICIAL INTELLIGENCE

This module develops students' understanding of research methodologies relevant to computer science. Topics include literature review, data collection, and ethical considerations in research. Students will learn to design experiments, analyse quantitative and qualitative data, and present findings using appropriate academic conventions. The module prepares students for independent research and project work in later stages of the programme.

This module advances students' programming skills through the study of complex data structures, algorithms, and design patterns. Emphasis is placed on concurrent programming, exception handling, and performance optimisation. Students will gain experience with GUI development, automated testing, and version control systems. The module aims to enhance problem-solving abilities and code quality in software development.

This module explores the theoretical and practical aspects of machine learning, covering both supervised and unsupervised approaches. Students will study regression, classification, clustering, and dimensionality reduction techniques. The curriculum includes model evaluation, overfitting, and ensemble methods. Emphasis is placed on the application of algorithms to real-world data and the investigation of neural network models.

This module introduces the fundamental techniques of natural language processing, including text preprocessing, syntactic parsing, and entity extraction. Students will explore language models, embeddings, and sentiment analysis. The curriculum covers machine translation, text classification, and speech recognition systems. Practical applications and evaluation of NLP methods are emphasised throughout the module.

This module examines the characteristics and challenges of big data, focusing on scalable data processing and analytics tools. Students will study distributed computing frameworks, data wrangling, and NoSQL databases. The curriculum includes data visualisation, stream processing, and ethical considerations in analytics. Emphasis is placed on applying analytical techniques to large and complex datasets.

This module provides a comprehensive overview of database systems, including relational and NoSQL models. Students will learn about schema design, advanced SQL querying, and transaction management. The curriculum addresses database security, optimisation, and data integrity constraints. Practical skills in database development and administration are developed through applied exercises.

You will gain valuable work experience on a 48 week work placement. This is an exciting opportunity to put what you have learned into practice, broaden your experience and demonstrate your abilities to potential employers.

This module enables students to undertake an independent project addressing a substantive problem in computer science or artificial intelligence. Students will define project objectives, conduct a literature review, and develop a technical solution using appropriate methodologies. The module includes project management, implementation, and evaluation components. Emphasis is placed on critical analysis, reflective practice, and effective communication of results.

This module focuses on the principles and applications of deep learning, including neural network architectures and optimisation techniques. Students will study convolutional and recurrent networks, model tuning, and the use of modern frameworks. The curriculum covers generative models and advanced topics in deep learning. Practical experience is gained through the development and evaluation of deep learning solutions.

This module explores the integration of robotics and computer vision, covering kinematics, sensor fusion, and motion planning. Students will study digital image processing, object detection, and SLAM techniques. The curriculum includes reinforcement learning and human-robot collaboration. Ethical and safety considerations in intelligent robotics are addressed throughout the module.

This module examines the ethical and governance challenges associated with artificial intelligence. Students will study ethical frameworks, bias detection, and transparency methods. The curriculum addresses legal, regulatory, and policy issues in AI deployment. Emphasis is placed on critical evaluation and the development of responsible AI practices.

This module covers the end-to-end lifecycle of machine learning operations, including model versioning, deployment, and monitoring. Students will study CI/CD pipelines, containerisation, and cloud-based deployment strategies. The curriculum addresses automation, retraining, and security in ML systems. Emphasis is placed on integrating MLOps practices into real-world machine learning workflows.