- Lecture 1 (Introduction):
- Chapter 1 (§ 1.1.3 excluded)
- Further (optional) material:
- Lecture 2 (linear regression and variable transformations):
- Chapter 2 (§ 2.1)
- Further material:
- Appendix A.3;
- R code.
- Lecture 3 (multivariate outcome and computational aspects):
- Chapter 2 (§ 2.2)
- Further material:
- Appendix A.1.
- Lecture 4 (overview of likelihood-based approaches):
- Chapter 2 (§ 2.3)
- Further material:
- Appendix A.2;
- R code.
- Lecture 5 (logistic regression):
- Chapter 2 (§ 2.4)
- Further material:
- Lecture 6 (optimism, overfitting and bias variance trade-off, cross-validation):
- Chapter 3 (§ 3.1, 3.2, 3.3, 3.4)
- Further material:
- R code (with supporting script)
- Lecture 7 (cross-validation, bootstrapping and information-based criteria):
- Chapter 3 (§ 3.5)
- Further material:
- Lecture 8 (methods for variable selection and principal components):
- Chapter 3 (§ 3.6)
- Further material:
- Lecture 9 (PCR, regularized regression with focus on lasso and ridge):
- Chapter 3 (§ 3.6)
- Chapter 4 (§ 4.1)
- Further material:
- Lecture 10 (kNN and kernels methods):
- Chapter 4 (§ 4.2)
- Lecture 11 (curse of dimansionality and splines):
- Chapter 4 (§ 4.3, 4.4)
- Further material:
- Lecture 12 (additive models, generalized additive models and projection pursuit):
- Chapter 4 (§ 4.5, 4.6)
- Lecture 13 (classification, performance evaluation, multivariate logistic models):
- Chapter 5 (§ 5.1, 5.2, 5.3)
- Further material:
- Lecture 14 (classification via linear model and discriminant analysis, with focus on LDA and QDA):
- Chapter 5 (§ 5.4, 5.5)
- Further material:
- Lecture 15 (regularized approaches for classification):
- Chapter 5 (§ 5.5)
- Further material:
- Elements of Statistical Learning (§ 4.3.1, 4.4.4, 4.4.5)
- R code
- Lecture 16 (regression trees):
- Chapter 4 (§ 4.8)
- Further material:
- R code (with supporting script and file)
- Lecture 17 (classification trees)
- Chapter 5 (§ 5.7)
- Further material:
- R code (with supporting script and file)
- Lecture 18 (neural networks and support vector machines)
- Chapter 4 (§ 4.9)
- Chapter 5 (§ 5.8)
- Further material:
- Lecture 19 (case study regression)
- Chapter 4 (§ 4.10)
- Lecture 20 (case study regression)
- Chapter 4 (§ 4.10)
- Lecture 21 (case study classification)
- Chapter 5 (§ 5.10)
- Lecture 22 (case study classification)
- Chapter 5 (§ 5.9, 5.10)
- Further material:
- Lecture 23 (unsupervised learning and dissimilarity)
- Chapter 6 (§ 6.1)
- Lecture 24 (non-hierarchical clustering with focus on K-means)
- Chapter 6 (§ 6.1)
- Further material:
- Lecture 25 (hierarchical clustering)
- Chapter 6 (§ 6.1)
- Further material:
- Lecture 26 (case study)
- Chapter 6 (§ 6.3)
- Lecture 27 (principal component analysis, multidimensional scaling)
- Chapter 10.2 of "The Introduction to Statistical Learning"
- Further material:
- Lecture 28 (case study)
- Lecture 29 (simulation of exam)
- Further material:
Forelesninger / Lectures
Publisert 13. jan. 2019 17:47
- Sist endret 15. mars 2023 11:09