This site uses cookies

Some of these cookies are essential, while others help us to improve your experience by providing insights into how the site is being used.

For more detailed information please check our Cookie notice

Necessary cookies

Necessary cookies enable core functionality. This website cannot function properly without these cookies.

Cookies that measure website use

If you provide permission, we will use Google Analytics to measure how you use the website so we can improve it based on our understanding of user needs. Google Analytics sets cookies that store anonymised information about how you got to the site, the pages you visit, how long you spend on each page and what you click on while you’re visiting the site.

Introduction to machine learning

24 Oct 2022 - 1 Nov 2022 1:00 pm - 3:00 pm
Data skills

What is machine learning? How is machine learning different from classic statistics? What are its applications? What type of models exist within machine learning?

If these are questions that you have then come along to this free three-part webinar that has been designed to deepen your understanding of the main concepts present within machine learning. Machine learning combines statistics and computer science to draw inference from patterns in data. It has now become an indispensable skill for data scientists and statisticians alike. These webinars will explore a few of the most important machine learning algorithms and then discuss model selection and evaluation of these models.

More specifically, our first session, on 24 October, 13.00 - 14.00, introduces the main concepts in machine learning. We tackle the big machine learning questions, like:

  • what is a ‘model’?
  • what is the difference between a supervised model and an unsupervised model?

Our second session, on 26 October, 13.00 - 14.00, moves on to exploring a specific unsupervised method, clustering. We will cover the following types of clustering algorithms:

  • centroid-based: specifically, k-means algorithm
  • hierarchical-based: divisive (top-down) and agglomerative (bottom-up)

We will then finish, on 1 November, with a live code demonstration in both R/RStudio and Python, using a customer dataset to put the theory into practice. Feel free to join the demonstration in your language of choice. The first half of the code demo, 13.00 - 14.00, will be in Python and the second half, 14.00 - 15.00, will be in R. See the materials under event resources below.

Presenters: Nadia Kennar and Louise Capener


This workshop is suitable for (higher) intermediate users of R and/or Python but there is no need to have experience with machine learning packages. Users should know how to set the working directory in R and/or Python, how to read in data, and how to save scripts and output files.

Depending on your language of choice, Python/Jupyter notebook and/or R and RStudio must be already installed and working.

Level: Session 1 and 2 (beginner), session 3 (Intermediate – requires some basic knowledge of Python and R)
Experience of R and/or Python: Yes
Knowledge of Machine Learning: No
Target audience: Researchers/data scientists/anyone interested in machine learning

Event resources

First session, 24 October:

Presentation slides

Webinar recording

Second session, 26 October:

Presentation slides

Webinar recording

Third session, 1 November:

Material for the code demo (GitHub)

Python demo recording

R demo recording