Robotics and Machine Learning is a series of advanced units that present in-depth knowledge of practical machine learning to perceive the surroundings and control real-time autonomous systems, including autonomous robots and scientific simulations. For learning outcomes, the students should establish their skills to describe multi-sensory-based perception in real-world scenarios, as well as gain the knowledge for the typical design of mobile robots, then evaluate and apply machine learning methods.

Fundamentals of Visual Computing (CM20219) is an introductory unit that teaches the essential mathematical and computational foundations for Computer Graphics and Vision, and how to apply them in practice with Python and WebGL programming. For learning outcomes, the students should establish their skills to model and manipulate graphical objects to generate images, then select appropriate techniques to process images and extract useful information. For the course materials, please refer to the [Moodle Page].

Machine Learning 1 (CM50264) is an MSc unit that presents algorithmic approaches to Machine learning and their advanced topics. After this unit, the students should display a systematic knowledge of algorithmic approaches and demonstrate a comprehensive understanding of their application to real-world problems. The students should also produce implementations as well as evaluate critically the relative merits and limitations of algorithmic approaches. For the course materials, please refer to the [Moodle Page].

Humans and Intelligent Machines (CM50272) is an advanced unit about intelligent robots, their interactions to humans, and the related ethical issues. After this unit, the students should understand current practice in systems involving humans and intelligent machines, and show awareness of systems design issues. The students should also establish skills to conduct new systems involving humans and machine intelligence. For the course materials, please refer to the [Moodle Page].

Intelligent Control and Cognitive Systems (CM30229/CM50230) is an advanced unit about the construction of intelligent systems and the potential issues in the deployment. After this unit, the students should achieve the roles and capabilities of conducting research and providing advice for companies, organisations, or citizens that ask for their help or opinion on the related topics in intelligent systems. For the course materials, please refer to the [Moodle Page].

There would be some project proposals from time to time that related to our current research. Most of them would lie in one of the following topics like Visual SLAMs, Dynamic Scene Reconstruction, Imitation Robot Learning, and Robot Motion Control. For more details, please refer to [FYP-CM30082] [MSc-CM50175]. In fact, we appreciate any new thought on top of our research, and are happy to help for making this happen. Please [Get In Touch] and discuss your exciting ideas.

LEGO is the most popular brick toy that joys our childhood. We would like to research deeper into data-driven solutions to (1) extract the hidden principles of the design of popular LEGO sets and the potential automated synthesis; (2) automatically build DUPLO sets using industrial robot arms; (3) predict the potential of financial investment before the LEGO sets are retired. For more details, please refer to [FYP-CM30082] [MSc-CM50175]. In fact, we always appreciate any new ideas to this field, and are happy to help for making it happen. Please [Get In Touch] if you wish to discuss your ideas.

Team Bath Racing Electric Autonomous (TBRe-AI) is an award-winning team that aims to deliver a driverless formula one vehicle for international competitions. We are supposed to develop a typical pipeline that includes several key components, i.e. Perception, SLAMs, Planning, Navigation and System Simulation. The candidates should select one of these core projects and work toward the integrated vehicle system and real-world evaluation. For more details, please refer to [FYP-CM30082] [MSc-CM50175].