Introduction to Machine Learning

Fundamentals of Machine Learning for Health
and Care Using R

Summary of the course

In this course we will introduce the basic ideas and algorithms of supervised learning and we will implement them using R programming language. A brief theoretical overview of the so-called learning setting will be provided, then the main focus will be on showing practical analysis and modelling of data related to healthcare.

Learning outcomes

• To understand concepts of machine learning for healthcare and compare and test a range of techniques.
• To classify features of data sources, analysing and interpreting the outputs of machine learning techniques in the context of practical solutions in the area of healthcare.

Detailed Programme

Day 1: Introduction
What is machine learning? Types of machine learning. Classification and regression. Training and test sets. Model evaluation. Over-fitting. Overview of Machine Learning Algorithms. No free lunch theorem. Cross validation. Practical Examples

Day 2: Data Preparation and Feature Engineering
Data analysis and pre-processing, exploratory data analysis, handling missing data. Feature engineering techniques including but not limited to: transformations, feature extraction, reduction and selection.

Day 3: Regression (part 1)
Single and multiple regression. Linear and polynomial regression. Parameter estimates. Residual analysis. Metrics for model evaluation. Plots and predictions. Feature selection.

Day 4: Classification

• Logistic Regression: why logistic regression; logistic function; simple logistic regression; multiple logistic regression (tentative); ROC curve; feature interpretation; predictions using logistic regression.
• Decision Trees: classification using decision trees; understanding and visualising decision trees; advantages and disadvantages of decision trees; predictions.
• Random Forests: from decisions trees to random forests; training and tuning random forests; predictions.

Day 5: Regression (part 2)
Using decision trees and random forests for regression. Introduction to regularisation (Ridge, LASSO and Elastic Nets).

Trainers

Your training will be led by:

• Filippo Cavallari, Data Science Lecturer, Data Science Campus, Office for National Statistics | Swyddfa Ystadegau Gwladol and
• Penny Holborn, Head of Faculty, Data Science Campus, Office for National Statistics | Swyddfa Ystadegau Gwladol