MSc Machine Learning in Science

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Tuition fee
Apply by
Start date
Duration
Campus
Jubilee Campus
UON University Park Campus
Mode of study
Fees and deadlines depend on the selected options. Fees and currency conversion are approximate.
Offer response
4 - 6 weeks after your application is submitted
Backlogs accepted
This course accepts backlogs

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:

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.

Find out what our graduates say about the course on our Physics Blog.

You will have the opportunity to develop your own research project on a topic of your choice. Previous projects have looked at:

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Requirements

The requirements may vary based on your selected study options.





















Modules

  • Compulsory modules
  • Machine Learning in Science – Part 1
  • Machine Learning in Science – Part 2
  • Machine Learning in Science – Project
  • Options modules
  • Big Data and Cloud Computing
  • Designing Intelligent Agents
  • Computer Vision
  • Professional Ethics in Computing
  • Introduction to Practical Quantum Computing
  • Neural Computation
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    Use our magical AI system, to check your admission chances for this course.
    Tuition fee
    Apply by
    Start date
    Duration
    Campus
    Jubilee Campus
    UON University Park Campus
    Mode of study
    Fees and deadlines depend on the selected options. Fees and currency conversion are approximate.
    Offer response
    4 - 6 weeks after your application is submitted
    Backlogs accepted
    This course accepts backlogs