Course summary
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.
- Galaxy Cluster Emulation
- Assembly of large scale structure in the Universe
- Application of ML to Fintech.
Modules
Our core modules will provide an introduction to the main concepts and methods of machine learning. You will then go on to cover more advanced topics of machine learning and neural networks. You will carry out a substantial investigation in the form of a research project on the application of the machine learning techniques learned as part of the course to a scientific problem. The study will be largely self-directed, with oversight and input provided by a supervisor from the School of Physics and Astronomy, School of Computer Science or School of Mathematical Sciences. The topic will be chosen from a list of potential projects provided by the schools in the Faculty of Science. The topic could be based on a theoretical and/or computational investigation, a review of research literature, and/or a combination of the two. We offer a range of optional modules in fascinating topics, such as computer vision, quantum information science, and big data and cloud computing.
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.
Fees and funding
Tuition fees
No fee information has been provided for this course
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
No additional fees or cost information has been supplied for this course, please contact the provider directly.
Provider information
University of Nottingham
University Park
Nottingham
NG7 2RD