Artificial Intelligence and Machine Learning in Science MSc

Artificial Intelligence and Machine learning have become a revolutionary force in science and medicine, contributing to numerous important breakthroughs in recent years. From climate change to drug discovery to particle physics, we are witnessing unprecedented advances in automation, data analysis, predictive modelling, and simulations.This cross-disciplinary masters programme will teach you the fundamentals of AI and Machine Learning, and how these can be applied to real-world scientific problems.Develop the coding and programming skills required to apply artificial intelligence to a wide range of fields. Work with real datasets from our world-leading research with links to organisations such as CERN, NASA and LIGO.Study the essential mathematical concepts that underpin artificial intelligence and machine learning such as probability, statistics and time series. Learn from expert academics, including former industry practitioners, Fellows of the Alan Turing Institute and members of Queen Mary’s Digital Environment Research Institute (DERI). No programming experience required.What you'll studyThis MSc will teach you the fundamental concepts of Artificial Intelligence with a focus on the theoretical knowledge and practical skills required to apply machine learning techniques to a variety of scientific industries, research areas and real-world problems. Our core compulsory modules explore probability, statistics, machine learning and deep learning, providing you with a solid foundation in AI. You will also gain experience with leading tools, software, and programming languages such as R, Python, C and SQL, among others. You will also work closely with a Queen Mary researcher on a research project in which you can apply your AI and Machine Learning skills to a high-profile dataset or active research being conducted in areas such as computational chemistry, epidemiology, renewable energy and many more.

A good 2:2 (55% or above) or above at undergraduate level in Mathematics, Physics, Chemistry, Computing, Engineering or any other STEM subjects.