Course log sf2935
Supporting material and lecturers' slides will be given on this page.


  • 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 .



[Kurshemsidan]     [Kursförteckning]     [Avdelningen Matematisk statistik]
Sidansvarig: Timo Koski
Uppdaterad: 2017-11-01