Course log sf2935
Supporting material and lecturers' slides will be given on this page.
- Lecture 20. High Dimensional Learning II pdf .
-
Summary of a Scientific paper
- Questions for the Summary link .
- Lecture 19. Guest lecture 2 by Erik Aurell pdf .
- Questions for the Summary link .
- Lecture 18. High dimensional learning I pdf .
- Lecture 17. Guest lecture 1 by Erik Aurell pdf .
- Lecture 16. Random Forests pdf .
- Lecture 15: Guest lecture by Lukas Käll. Instead of slides we give the two papers below
- Lecture 14. Unsupervised Learning II pdf .
- Lecture 13. Unsupervised Learning I pdf .
- Lecture 12. Data Science at Klarna Questions for summary .
- Supplementary reading about logistic regression (lecture slides from 2016)
pdf .
- Lecture 9 and Lecture 11 on Bayesian learning: pdf .
- Lecture 8. Support vector machines pdf .
- Lecture 7. Exponential family of distributions and deep exponential nets pdf .
- Supplementary reading about deep exponential nets and sigmoid belief nets
pdf .
- Lecture 6. Neural networks and statistics pdf .
- Supplementary reading about neural networks and statistics
pdf .
- Lecture 5. Introduction to R pdf .
- Lecture 4. Bootstrap pdf .
- Lecture 3. LDA, QDA, Nearest neighbor classifiers pdf .
- Lecture 2. Supervised Classification and Linear Discriminants pdf .
- Lecture 1. Perceptrons and feedforward neural networks pdf .
- Linear Vector Spaces (supporting material) pdf .
|
