Introduction to Machine Learning

Chris Yeomans and Jiangjiao Xu

05 May 2020

Preface

This course is based on original material by JJ Valetta from Easter 2019

An introductory workshop on the field of machine learning. The focus will be on how to use these methods in practice using R and Python, rather than on the rigorous underlying mathematics. The target audience is anyone who wants to know what machine learning is, what problems it can solve and how we can solve them in practice using R or Python.

Prerequisites

• Programming basics in either R or Python

Learning outcomes

• Understand the key concepts and terminology used in the field of machine learning
• Build predictive models for clustering, regression and classification problems
• Apply machine learning algorithms in R/Python to a variety of real-world datasets
• Recognise practical issues in data-driven modelling

Recommended reading

I highly recommend the following books:

• An Introduction to Statistical Learning
• The Elements of Statistical Learning
• Pattern Recognition and Machine Learning
• Machine Learning: A Probabilistic Perspective

Software packages

• R: caret
• Python: scikit-learn

Please make sure to read the documentation of any machine learning algorithm before using it!

Data files

All data files can be downloaded as a ZIP file from here.