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MSc Machine Learning in Science

  • DeadlineStudy Details:

    MSc Full-time: 12 months Part-time: 24 months

Masters Degree Description

The 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 techniques
  • apply statistical and physical principles to break down algorithms, and explain how they work
  • design strategies for applying machine learning to the analysis of scientific data sets.

Entry Requirements

2.1 (or international equivalent) in one of the following areas: physics, mathematics, computer science, chemistry, engineering. A 2.2 (or international equivalent) may be considered if the applicant has relevant work experience or another supporting factor.

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Fees

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Student Destinations

Machine learning and artificial intelligence have become central for the economy and society. Graduates are highly sought after in data intensive sectors, including IT, finance, consultancy, manufacturing, as well as academic and industrial research and development.

95.5% of undergraduates from the School of Physics and Astronomy secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £34,063.

Module Details

Core modules

  • Machine Learning in Science – Part 120 credits
  • Machine Learning in Science – Part 220 credits
  • Applied Statistics and Probability20 credits
  • Machine Learning in Science – Project60 credits

Optional modules

  • Introduction to Practical Quantum Computing10 credits
  • Computer Vision20 credits
  • Designing Intelligent Agents20 credits
  • Neural Computation
  • Big Data Learning and Technologies20 credits
  • Simulation and Optimisation for Decision Support20 credits
  • Linear and Discrete Optimisation20 credits
  • Handling Uncertainty with Fuzzy Sets and Fuzzy Systems

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