Course overviewThe development and use of machine learning (ML) and artificial intelligence (AI) have revolutionised areas such as computer vision, speech recognition and language processing.On this course you will learn how to apply ML and AI techniques to real scientific problems. This will help you build vital skills, enhancing your employability in a rapidly expanding area.Graduates of this course will learn how to:identify and use relevant computational tools and programming techniquesapply statistical and physical principles to break down algorithms, and explain how they workdesign strategies for applying machine learning to the analysis of scientific data sets.In addition, you will develop a broad set of transferable skills, including communication, critical thinking, and problem-solving. Previous students of this course have undertaken paid part-time internships with external partners.You will have the opportunity to develop your own research project on a topic of your choice. Previous projects have looked at:Deep Learning for drug discoveryMachine Learning for sustainable solvent selectionQuantum reinforcement learningSupervised machine learning on a quantum computerDeep Learning network for fatigue monitoring of wind turbine bladesShaking all over – vibration cancellation at the atomic levelUsing machine learning to automatically segment the placenta from pregnancy MRI scansMachine learning assisted high-throughput computational screening of metal organic frameworks for biogas upgradingSimulating the UniverseDetecting dark matter substructure in galaxiesPersonalised modelling of cerebral blood flow from multi-modal features for early detection of dementiaMachine Learning natural product biosynthesisAdvanced natural language processing in Fintech