Practical Machine Learning in R

This 4-day course builds on pre-existing R expertise by providing a blend of theory and applied practice in different machine learnings methods commonly used in computational biology. The course encourages an interactive environment allowing delegates to openly query machine learning methods in the context of their own research, hence places are very limited. The next course dates are Tues 28th Feb - Fri 3rd Mar 2023.

After a successful beta run, we are very excited to open this course to the public for the first time. The course will be run online in an interactive environment with plenty of contact time.

The theory of this course includes topics on:

  • Machine learning basics
  • Regression
  • Decision trees incl. random forest
  • Variable selection in a variety of methods
  • Model assessment - including under/over fitting discussions
  • Gradient boosting
  • Clustering

The course practical sessions will focus on the following topics:

  1. Model assessment
  2. Linear models
  3. Decision Trees
  4. Bagging Random Forests & Gradient Boosting
  5. Variable Selection

This course has time for plenty of active discussion and reflection. We encourage you to bring your own ML ideas/data problems to discuss on the last day.

Administrative information

  • This course requires prior knowledge of R. Places are extremely limited and will be offered based on the answers from the registration form.
  • Fees are £500 for academic delegates, £700 for delegates from public institutions (not academic). Industry delegates may submit an application and will be quoted in individual basis.
  • If you are a delegate from the University of Liverpool you can access student bursaries. You have to submit an application to the bursary together with your registration. Link to the bursary can be accessed from the registration form.


 What did our delegates have to say about this course?

This course was extremely useful in particular the help that Dan Green provided creating a dummy dataset to mimic my research data where I could see and discuss directly applicable options to my own research. UoL PhD student (S.M) verbal feedback (not verbatim)


Back to: Computational Biology Facility