BSc (Hons) Artificial Intelligence Bachelor's degree at University College Birmingham

This module provides an introduction to the foundational concepts of computer science, including the evolution of computing, core hardware components, and operating systems. Students will explore binary data representation, algorithms, and basic logic circuits. The module also covers essential networking concepts and principles of data storage and security. Emphasis is placed on developing a broad understanding of the key elements underpinning modern computing systems.

This module introduces students to the fundamental principles of programming using contemporary languages and tools. Key topics include variables, control structures, functions, and object-oriented concepts. Students will develop skills in problem-solving, debugging, and code documentation through practical exercises. The module aims to establish a solid foundation for further study in software development and computational thinking.

This module examines the core principles and historical development of artificial intelligence. Students will study problem-solving paradigms, search strategies, and knowledge representation techniques. The curriculum includes an introduction to machine learning, neural networks, and natural language understanding. Ethical considerations and the societal impact of AI are also explored in depth.

This module covers essential mathematical concepts required for computing, including set theory, logic, and matrix operations. Students will engage with probability, statistics, and graph theory as they relate to computational problems. The module provides a foundation in number theory and Boolean algebra for application in computer science. Emphasis is placed on developing analytical skills for problem-solving in technical contexts.

This module introduces the fundamental principles of cybersecurity, focusing on confidentiality, integrity, and availability. Students will examine common cyber threats, cryptographic methods, and authentication mechanisms. The curriculum addresses network defence, malware, and organisational security policies. Legal, ethical, and risk management considerations are integrated throughout the module.

This module provides an overview of computer networking concepts, including network topologies, protocols, and addressing schemes. Students will study the OSI and TCP/IP models, subnetting, and key network devices. The module covers both wired and wireless transmission media, as well as core application protocols. Practical skills in network troubleshooting and security are developed through applied activities.

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.