Course overview
This course covers modern machine learning methods particularly useful for large and complex health data Content includes: Linear Regression and K -Nearest Neighbours; Classification (logistic regression, linear discriminant analysis); Resampling Methods (Cross-Validation, Bootstrap); Model Selection and Regularization (subset selection, shrinkage methods, dimension reduction methods); Beyond Linearity (fractional polynomials, basis functions, splines, generalized additive models); Tree-Based Methods (decision trees, bagging, random forests, boosting).
Course learning outcomes
- Recognise situations where machine learning methods can offer advantages over traditional statistical modelling approaches to data analyses in health applications
- Recognise and explain the differences between the goals of description and prediction
- Determine and implement appropriate machine learning approaches for description and prediction in real-world health applications
- Measure and explain the uncertainty of the results of analyses using machine learning approaches
- Interpret the results of analyses using machine learning in light of the assumptions required, the quality of input data, and the sensitivity to the specific technique implemented
- Critically appraise published papers concerning machine learning applications for classification or prediction in health
- Effectively communicate results of analyses in language suitable for a clinical or epidemiological journal