Centre for Research Informatics Training
Principles of Machine Learning
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Event details
Sessions
- 22 April 2027 — 09:30 to 17:30 — Craik-Marshall
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About the course
Machine learning provides a powerful set of methods for identifying patterns, making predictions, and extracting insights from complex datasets. This course provides an introduction to the principles and practice of machine learning for participants with no prior experience in the subject.
Participants will learn the key concepts and terminology used in machine learning and gain practical experience applying machine learning methods using the CARET package in R. The course introduces a range of commonly used supervised and unsupervised learning algorithms, as well as dimensionality reduction techniques.
The emphasis of the course is on understanding how to apply machine learning methods appropriately, interpret their outputs critically, and avoid common pitfalls rather than on the mathematical details of how the algorithms work.
By the end of the course, participants should be comfortable with the terminology, workflows, and key principles of machine learning and be able to apply and interpret common machine learning approaches in practice.
Teaching is primarily hands-on, with short presentations and demonstrations introducing the concepts and methods needed to apply machine learning techniques to data.
Intended audience
This course is suitable for:
- researchers and students with no prior experience of machine learning
- participants who want an introduction to machine learning concepts and terminology
- researchers interested in applying machine learning methods to biological or other research datasets
- participants with experience in R and classical statistics who wish to extend their analytical skills to machine learning
Prerequisites
Participants should have:
- coding skills equivalent to those covered in the Data Analysis in R course
- knowledge of classical statistics equivalent to that covered in the Core Statistics course
Course fees
All fees are per full training day
| Category | Fee |
|---|---|
| Industry full charge | £130.00 |
| Academic / Government / charity concessionary | £65.00 |
| Cambridge University staff members / postdocs / visitors | £65.00 |
| Cambridge University registered students | Free |
| Special events | Per event |
Payment options will be provided in booking confirmation emails sent after registration.