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Artificial Intelligence and Machine Learning in Science (Taught)

Course details
  • 2 Study options
  • Postgraduate
Course location
Mile End

Course summary

Introduction
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.

Programme highlights

  • 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.

Career outcomes
The demand for qualified AI and Machine Learning experts is growing and we are witnessing an explosion of AI opportunities in London. From environmental science to healthcare, organisations are seeking skilled graduates with hands-on coding and programming skills. Given the broad scope of this MSc and the focus on developing strong theoretical and practical skills, graduates may wish to consider other sectors such as communications, finance or retail.

Highly sought-after Machine Learning Engineers can earn over £60,000 per year in the UK (Source: Indeed).

How to apply

Open days

Fees and funding

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Course options

Sponsorship information

To learn more about funding and scholarships, please visit our funding a masters webpage at: www.qmul.ac.uk/postgraduate/taught/funding_masters/

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