Machine Learning in Science

Entry requirements

A 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.

IELTS: 6.5 with at least 6.0 in any element.

Months of entry

September

Course content

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

In addition, you will develop a broad set of transferable skills, including communication, critical thinking, and problem-solving.

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

• Galaxy Cluster Emulation
• Assembly of large scale structure in the Universe
• Application of ML to Fintech