| layout | default |
|---|---|
| title | To Do |
- In Common steps tutorial: PCA scale: needs to use scaling data (not at the moment)
- In Common steps tutorial: need change running of RF, now it’s based on test data!
- In Common steps tutorial or bagging / boosting / stacking: number of trees in RF is not tunned, add that (check also XGBoost)
- Be clear about the course being mostly for omics analyses
- Add basic ML to course prerequisites
- Prepare primer on regularized methods, KNN, Random Forest, data splitting and ask people to study before the course
- Prepare quiz checking understadning based on the primer, and even better, the entire Introduction to Biostatistics course
- Common steps: add example of questions and answers around linear and logistics regression outputs
- Common steps: add equations to all model evaluation metrics
- Bagging, boosting, stacking lab: visualize XGBoost variable importance
- Have Python code for all the labs
- All chapters: check for missing learning outcomes, and preface text
- Survival presentation: explain more time varying and competing risk scores
- Survival lab: time varying a bit confusing example with start and stop intervals, maybe add one more example or replace this one
- Put mix-effect models in one session, check lab, and more examples if needed
- Have Introduction to Neural session in the Thursday afternoon
- On Friday, continue with NN session: ML in LS applications, with additional focus on NN architecture, and more labs with image analyses, transfer learning etc. (see what people are doing and adjust)
- Keep working in groups, in the morning let students discuss alone in the groups first!