Warning: Please be aware that these videos are a snapshot, and as such may use an outdated version of the tutorial and/or Galaxy. Below the video you will find links to the tutorials as they appeared at the time of recording.
Introduction to Machine Learning using R
Below are video tutorials for this GTN material, created for various (past) events.
Tutorial Video (February 2021)
This is an Introduction to Machine Learning in R, in which you’ll learn the basics of unsupervised learning for pattern recognition and supervised learning for prediction. At the end of this workshop, we hope that you will - appreciate the importance of performing exploratory data analysis (or EDA) before starting to model your data. - understand the basics of unsupervised learning and know the examples of principal component analysis (PCA) and k-means clustering. - understand the basics of supervised learning for prediction and the differences between classification and regression. - understand modern machine learning techniques and principles, such as test train split, k-fold cross validation and regularization. - be able to write code to implement the above techniques and methodologies using R, caret and glmnet. We will not be focusing on the mathematical foundation for each of the methods and approaches we’ll be discussing. There are many resources that can provide this context, but for the purposes of this workshop we believe that they are beyond the scope.