Course log sf2935
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. 8187 in the slides of
pdf
 Lecture 1314: support vector machines;
 slides
 Sections 35 of the linked tutorial as extra material
a paper
 Lecture 1112
pdf
 Lecture 910: 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 III below
 Lecture 7.pdf.
 Lecture 6.pdf.
 Lecture 4.pdf.
 Lecture 3.pdf.
 LINEAR PROGRAMMING DISCRIMINANT, (LPD) : pdf.
 Lecture 2.pdf.
 Lecture 1. pdf .
 BIASVARIANCE TRADEOFF : link .
 section 6.5.3. in A.Blum, J. Hopcroft and R. Kannan: Foundations of Data Science, 2016 (pdf) : click .
