Data science and machine learning represent an overlap of statistics, computer science and domain expertise and are increasingly becoming integral in research applications based on health and social data. The combination of statistics and computer science, allows machine learning methodology to take advantage of the strengths of both areas yielding methods that have some advantages over statistical analysis and computational algorithms on their own. The course will cover the most in demand data science and machine learning methods for both supervised (regression, classification and regression trees, neural nets and support vector machines) and unsupervised learning (clustering). Participants will learn how to choose the appropriate method, and how to analyse and interpret the results using a free interface to the popular data science and statistical package R.
Instructors: Professor Marijka Batterham, Professor Alberto Nettel-Aguirre, Brad Wakefield
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