Lecture plan MA8701V2023
This plan is tentative and may change!
Part 1: Core concepts (3 weeks: 09.01, 13.01, 16.01, 20.01, 23.01, 27.01)
- L1: Overview of course, getting to know each other (much compulsory work in groups), review on linear regression, decision theoretic framework for regression
- L2: Review of classification, decision theoretic framework for classification, bias-variance trace-off
- W2 (L3-L4): Model selection and model assessment results
- W3 (L5-L6): Missing data: mechanisms of missingness, single imputation methods including Bayesian linear regression, predictive mean matching, Rubins rules, fully conditional specification of imputation models.
Part 2: Shrinkage and regularization in LM and GLM (3 weeks: 30.01, 03.02, 06.02, 10.02, 13.02, 17.02,20.02)
- L7: Ridge for linear models
- L8: Lasso for linear models
- L9: Variants of lasso and ridge
- L10: Ridge and lasso for logistic regression (and GLM)
- L11+L12: Statistical inference for lasso/ridge and selective inference
Part 3: Ensembles (4 weeks: 24.02, 27.02, 03.03, 06.03, 10.03, 13.03, no lecture 17.03 )
- 24.02: L13 Wisdom of the crowds, bagging, trees, random forest
- 27.02: L14 Adaboost and gradient tree boosting
- Watch video 03.03 by yourself (so no lecture) - link to talk by Berent Lunde in MA8702021V on Xgboost will be sent to participants
- 06.03: L15 Stacked ensembles
- 10.03: L16 Hyperparameter tuning
- 13.03: L17 Uncertainty assessment
Part 4: Explainable AI/Interpretable Machine Learning General introduction to the topic, partial dependence plots, ALE plots, LIME, Shapley values and counterfactuals.
- 20.03: L18 digitally on zoom with Kjersti from Oslo
- 24.03: L19 digitally on zoom with Kjersti from Oslo
- 27.03 (10.15-12) L20 physically in S21 with Kjersti in Trondheim
No lecture 31.03 - happy easter!
Part 5: Closing
- Article presentations: 14.04, 17.04, 21.04
- Summing up lecture: 24.04