Course Description

This will be a two-week short course taught by several experts both locally and internationally. The aim of the course is to enhance capacity in machine learning methods for health data science to address global health problems in Africa.

In week 1, participants will be introduced to several machine learning methods and techniques in a sequence of lectures and practical exercises involving computer applications using relevant real data sets in the health domain.

In week 2, participants will be engage in specific case study applications using data sets that are of interest to them from their place of work or data sets shared by the instructors whichever is applicable. The focus would be on more advanced machine learning methods but still emphasizing the ideas covered in week 1.

Learning Objectives

At the end of the two weeks, participants would be able to apply the following machine learning methods and techniques to their health datasets or health domain of their interest:

1. Understand machine learning notations, terminologies, and performance metrics.
2. Use Logistic regression, Naïve Bayes, kNN, QDA and LDA to model health data.
3. Differentiate between Regression and classification trees and use random forest techniques to classify data.
4. Use SVM, Multinomial logistic regression and Regularization techniques in modelling health data.
5. properly compare models using appropriate statistical approaches for fair model comparison.
6. Interpret machine learning results and write reports on them.

Course Website

GitHub