The Machine Learning with R module provides Ph.D. students with the basics of Machine Learning using a hands-on lab approach and application-oriented. The first part of the course will look into how conventional statistical analysis relates to Machine Learning, and make a comparison of each. We will then focus on Unsupervised Learning, exploring the most common techniques, from Clustering and Cluster-Validation to Dimensional Reduction, discussing the advantages & disadvantages of each algorithm. We will then concentrate on Supervised Learning, using some of the most popular algorithms and introducing the concepts of Classification, Training and Testing Split, Neural Network, Support Vector Machine, Feature Extraction & Selection. We will also consider how to present the results of the analyses mentioned above by exploring different data visualization techniques. On completion of the Machine Learning module students will be expected to have a good understanding of the fundamental issues and challenges of these topics: e.g.; possess practical knowledge of Supervised and Unsupervised approaches, know strengths and weaknesses of the most popular techniques, be able to implement various algorithms in a range of realistic research applications.