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


    • Guest Lecture Erik Aurell: Model learning using (many) biological sequences
      • Protein structures pdf
      • Epistasis pdf
      • Supplementary material: exponential families pdf
      • Additional material: Christoph Feinauer, Marcin J. Skwark, Andrea Pagnani, Erik Aurell, Improving contact prediction along three dimensions PLoS Comput Biol 10(10): click or pdf
      • Question 5 to be answered in submitted Summary: Explain how the exponential family of probability distributions is related to this problem.
      • Question 4 to be answered in submitted Summary: Explain the mean field method of approximation for contact prediction
    • Lecture 18: Random Forests pdf
    • Lecture 17: Unsupervised learning part 2 pdf
    • Lecture 16: Unsupervised learning part 1 pdf
    • Lecture 15: an exercise class on pp. 81-87 in the slides of pdf
    • Lecture 13-14: support vector machines;
      • slides
      • Sections 3-5 of the linked tutorial as extra material a paper
    • Lecture 11-12 pdf
    • Lecture 9-10: Bayesian Learning pdf
    • Paper: Approximate Bayesian Computing pdf .

      • Question 3 to be answered in a submitted Summary: Explain the basic ABC = Approximate Bayesian Computation for Bayesian model evidence as found in the linked paper: pdf .

    • Lecture 8.: Guest lecture by Lukas Käll. In addition there are two connected papers I-II below
    • Lecture 7.pdf.
    • Lecture 6.pdf.
    • Lecture 4.pdf.
    • Lecture 3.pdf.
    • LINEAR PROGRAMMING DISCRIMINANT, (LPD) : pdf.
    • Lecture 2.pdf.
    • Lecture 1. pdf .
      • BIAS-VARIANCE TRADEOFF : link .
      • section 6.5.3. in A.Blum, J. Hopcroft and R. Kannan: Foundations of Data Science, 2016 (pdf) : click .



[Kurshemsidan]     [Kursförteckning]     [Avdelningen Matematisk statistik]
Sidansvarig: Timo Koski
Uppdaterad: 2016-10-31