Lectures
- Lecture 1 (slides / handout)
- Lecture 2 (slides / handout)
- Chapter 3: sections 3.2, 3.3
- Chapter 7: sections 7.1, 7.2, 7.3, 7.4, 7.5, 7.7
- Lecture 3 (slides / handout)
- Chapter 3: sections 3.4, 3.5
- Chapter 7: sections 7.10, 7.11
- Lecture 4 (slides / handout)
- Chapter 3: sections 3.4, 3.6, 3.8
- Chapter 4: subsection 4.4.4
- Zou (2006)
- Lecture 5 (slides / handout)
- Lecture 6 (slides / handout)
- Chapter 5: sections 5.1, 5.2, 5.4, 5.5, 5.7
- Lecture 7 (slides / handout)
- Chapter 8: section 8.7
- Chapter 9: sections 9.1, 9.2
- Lecture 8 (slides / handout)
- Chapter 13: section 13.4
- Chapter 15: sections 15.1, 15.2, 15.3, 15.4
- Lecture 9 (slides / handout / notes)
- Chapter 10: sections 10.1, 10.2, 10.3, 10.4, 10.5, 10.10, 10.12.1
- Lecture 10 (slides / handout)
- Lecture 11 (slides / handout)
- Chapter 16: sections 16.1, 16.2
- Chapter 18: sections 18.1, 18.3.5, 18.6
- Lecture 12 by Odd Kolbj?rnsen (slides)
- Lecture 13
- notes on multiple tests corrections;
- overview of the course's contents through a Kahoot game.
- Lecture 14 (PhD presentations)
- Variable selection in ultra-high dimensional mixed models
- Machine learning of coarse-grained molecular dynamics force fields
- Interpretable factor models of single-cell RNA-Seq via variational autoencoders
- Finding new medication strategies for blood cancer patients
- Example of data analysis in high energy physics
- Lecture 15 (PhD presentations)
- Hybrid particle-field theory for biological macromolecules: Advances in computational methodology
- Boosting multi-step autoregressive forecasts
Published Aug. 13, 2020 4:40 PM
- Last modified Nov. 8, 2021 3:57 PM