Executive Education in AI and Machine Learning
The University of Exeter offers both bespoke and standardised executive education packages to support upskilling your staff so they can use the latest developments in AI and Machine Learning tools. Please get in touch if you’d like to discuss your requirements.
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Machine Learning Foundation Course
Machine Learning Foundation is an introductory course that gives learners the opportunity to learn core Machine Learning theory from University of Exeter lecturers and put these principles into practice with live data right away.
The course is made up of 8 teaching days (either online or in person) and 24 hours of self-led study, with project support meetings available to ensure learners have all the guidance they might need.
At the end of the course there’s an opportunity to share work either in person or remotely; all learners will have a tangible example of your new skills to take away. The course has previously been delivered to climate scientists at the Met Office’s Hadley Centre.
The course is designed for a broad range of staff working with data. Learners will need to be reasonably proficient with data and comfortable handling large data sets.
To study on the course we will be using your IT equipment, Python and Jupyter notebooks. If staff have not used Python before please let us know when you enrol: resources are available to support you to skill up in advance including additional courses.
It’s a good idea once staff are enrolled to start thinking about their projects and the data they might need to source right away, to ensure this is ready in good time. The course leaders will be able to advise on whether projects sound both challenging enough and achievable in the time.
Machine Learning Foundation modules:
- Learning From Data
- Supervised Machine Learning Regression
- Data Wrangling
- Supervised Machine Learning 2: Classification
- Neural Networks
- Unsupervised Machine Learning
- Responsible AI
- Advanced Machine Learning For Your Sector
The course has been designed so that all learners will leave with working proficiency in Machine Learning.
We can provide all learners who complete 75% of taught sessions, and submit a project, with a completion certificate and digital badge to add to email signatures.
All learners will leave with a project output they can use as evidence of their proficiency.
Adaptable to your sector's needs
We are happy to hear about your requirements and develop the course content to ensure it is relevant to your sector and paced for your learners’ needs- if you would like to learn more or discuss any adjustments please just get in touch.
The team
Academics who have previously delivered these programmes include:
Professor Hywel Williams
Professor of Environmental Data Science
Hywel focusses on problems that link social processes and environmental change.
Dr Stefan Siegert
Associate Professor of Data Science
Stefan specialises in applied statistics and data science, particularly the development models for environmental statistics and forecasting using computer simulation models.
Professor Richard Everson
Professor of Machine Learning
Richard has worked at Brown, Yale and Imperial. His research interests are in machine learning, statistical pattern recognition, multi-objective optimisation and the links between them.
