Supervised-Machine-Learning

(Supervised) Machine Learning Instructor: Paul Clough (paul.clough@peakindicators.com | p.d.clough@sheffield.ac.uk) This session will introduce libraries and functions in R for performing Machine Learning (ML). Machine Learning is typically viewed as a sub-field within Artificial Intelligence (AI): “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” (Oxford English Dictionary, 2018). The most common forms of ML are: (i) supervised learning (e.g., prediction/regression and classification); (ii) unsupervised learning (e.g., clustering); and (iii) reinforcement learning. This session will focus on supervised ML. We will start by reviewing linear regression that forms a good starting point for understanding machine learning and may be something you are already familiar with. Then we will explore further algorithms that can be used for regression and classification. We will mainly focus on using the caret package for ML, but (as usual) there are many ways of doing things in R and multiple packages that can be used for ML1 . Note: this session is very much a hands-on overview of supervised machine learning and some of the R functions that can be used. For a more theoretical overview you might find the book “An Introduction to Statistical Learning with Applications in R” and the accompanying videos helpful2