About the course | Intended audience | Prerequisites | Content details
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.
Back to top 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
Content details
The course covers the following topics:
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Introduction to machine learning
Introduces the language and key concepts of machine learning and provides an overview of the broad processes involved in developing machine learning solutions. -
Machine learning workflows in R
Introduces the CARET package and demonstrates how it can be used to implement and compare machine learning algorithms in R. -
Supervised learning algorithms
Covers commonly used supervised learning methods, including nearest neighbours, support vector machines, random forests, and neural networks, and discusses their applications and interpretation. -
Dimensionality reduction
Introduces dimensionality reduction techniques, with a focus on principal component analysis (PCA), and demonstrates how these methods can be used to simplify and visualise complex datasets. -
Unsupervised learning
Covers unsupervised learning approaches and demonstrates how these methods can be used to identify patterns and structure within data. -
Interpreting machine learning results
Discusses how to critically evaluate machine learning outputs, recognise common pitfalls, and apply machine learning methods appropriately to real-world datasets.