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MSc Machine Learning in Science

  • DeadlineStudy Details:

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

Course 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

  • Machine Learning in Science – Part one
  • Machine Learning in Science – Part two
  • Machine Learning in Science – Project

Optional

  • Big Data and Cloud Computing
  • Designing Intelligent Agents
  • Computer Vision
  • Professional Ethics in Computing
  • Introduction to Quantum Information Science
  • The Physics of Deep Learning
  • Neural Computation

In addition, this course offers three alternative strands/pathways which allow you to select different combinations of core and optional modules to meet your interests.

Strand one core modules:

  • Machine Learning
  • Statistical Machine Learning

Strand two core modules:

  • Statistical Foundations
  • Fundamentals of Statistics

Strand three core modules:

  • Programming
  • Scientific Programming in Python

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