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The goal of this module is to put into practise the skills and knowledge learned over the previous three semesters, and develop an original solution to a complex problem. Students will develop group working skills, and use appropriate engineering methods to identify requirements, design a solution, and manage the delivery and test of the solution. An example of the type of project that could be tackled would be the development of an intelligent robot system, integrating aspects of hardware design, computer engineering, software, artificial intelligence, and signal processing.

Examples of the types of skills applied from other modules might include:
- Designing custom PCBs to integrate sensor and compute modules
- Writing embedded C code and assembly to interface algorithms and peripherals
- Creating custom digital IP blocks to accelerate computationally intensive functions
- Creating SoCs with multiple CPUs and IP cores to meet sensor, processing and actuator requirements
- Modelling and implementing control loops for managing system functions
- Selecting and customising learning algorithms to perform specific tasks
- Integrating low-level control loops and high-level planning to achieve goals
- Designing and implementing signal-processing pipelines in software and hardware
- Optimising software algorithms and implementations to meet performance and power constraints
- Managing on-device storage of training and classification data
- Using analysis and filtering to pre-process and combine data streams into features

The project is introduced in the first semester to run alongside systematic design, so that students have a concrete example to think about in that module. This also gives students time to think about the project from a design point of view, without immediately moving to implementation. The bulk of the hours will be spent in the second semester, where the project development, integration, and testing will occur.

The goal of this module is to put into practise the skills and knowledge learned over the previous three semesters, and develop an original solution to a complex problem. Students will develop group working skills, and use appropriate engineering methods to identify requirements, design a solution, and manage the delivery and test of the solution. An example of the type of project that could be tackled would be the development of an intelligent robot system, integrating aspects of hardware design, computer engineering, software, artificial intelligence, and signal processing.

Examples of the types of skills applied from other modules might include:
- Designing custom PCBs to integrate sensor and compute modules
- Writing embedded C code and assembly to interface algorithms and peripherals
- Creating custom digital IP blocks to accelerate computationally intensive functions
- Creating SoCs with multiple CPUs and IP cores to meet sensor, processing and actuator requirements
- Modelling and implementing control loops for managing system functions
- Selecting and customising learning algorithms to perform specific tasks
- Integrating low-level control loops and high-level planning to achieve goals
- Designing and implementing signal-processing pipelines in software and hardware
- Optimising software algorithms and implementations to meet performance and power constraints
- Managing on-device storage of training and classification data
- Using analysis and filtering to pre-process and combine data streams into features

The project is introduced in the first semester to run alongside systematic design, so that students have a concrete example to think about in that module. This also gives students time to think about the project from a design point of view, without immediately moving to implementation. The bulk of the hours will be spent in the second semester, where the project development, integration, and testing will occur.

This thought-provoking module introduces you to the surveillance and predictive AI technologies informing policy and practice in contemporary. criminal justice systems. Theory and research from the fields of criminology, criminal justice studies, sociology, law, and the interdisciplinary STS scholarship are used to explore the prospects, challenges, and governance of the technologies. To deepen and broaden your knowledge of the key issues, international comparisons are embedded throughout. Participation in this module does not require prior technical knowledge of AI design processes. The module focuses on their implementation, challenges, and governance. It is as such suitable for postgraduates from diverse professional and academic backgrounds.

This thought-provoking module introduces you to the surveillance and predictive AI technologies informing policy and practice in contemporary. criminal justice systems. Theory and research from the fields of criminology, criminal justice studies, sociology, law, and the interdisciplinary STS scholarship are used to explore the prospects, challenges, and governance of the technologies. To deepen and broaden your knowledge of the key issues, international comparisons are embedded throughout. Participation in this module does not require prior technical knowledge of AI design processes. The module focuses on their implementation, challenges, and governance. It is as such suitable for postgraduates from diverse professional and academic backgrounds.

In this module you will examine the socio-technical influence of Artificial Intelligence (AI) on governance practices and global economic systems. You will explore how AI shapes decision-making processes, labour markets, international trade, public policy, ethics, and security, while also highlighting how these in turn affect the data, intellectual resources and human engagement on which the success of AI depends. The module also explores the challenges related to trust, regulation, inequality, and global cooperation. The course will address both the theoretical and practical aspects of AI integration into governance and economic structures.

In this module you will examine the socio-technical influence of Artificial Intelligence (AI) on governance practices and global economic systems. You will explore how AI shapes decision-making processes, labour markets, international trade, public policy, ethics, and security, while also highlighting how these in turn affect the data, intellectual resources and human engagement on which the success of AI depends. The module also explores the challenges related to trust, regulation, inequality, and global cooperation. The course will address both the theoretical and practical aspects of AI integration into governance and economic structures.

The module will introduce the fundamentals of a range of important techniques, so that the way that these models learn from data is understood. Applications will also be shown of applying AI and ML to chemistry. Workshops will give hands-on experience with running and training ML models and these methods will be applied to specific examples as part of the mini-project.

This module will provide an overview of how machine learning and Artificial Intelligence can be used to answer questions in different fields of psychology.

This module will provide an overview of how machine learning and Artificial Intelligence can be used to answer questions in different fields of psychology.

This module will provide an overview of how machine learning and Artificial Intelligence can be used to answer questions in different fields of psychology.