Statistical Learning in Computational Biology

Statistical Learning in Computational Biology


  • 2015-08-12 Re-exams will be on the 18th of September. Write us an email, if you want to take the re-exam. If you have an important reason why this does not fit to your schedule, please send us an email. The schedule can be found in the password protected area.

General info

  • Lecturer: Nico Pfeifer
  • Tutor: Nora K. Speicher
  • Lecture: Thursdays from 10:00 - 12:00 at room 001 in building E2.1 (first lecture was on the 23rd of April)
  • Tutorial (every other week): Thursdays from 8:00 - 10:00. The first tutorial will be on the 7th of May (in room 021 in the MPI for Informatics building (E1.4)).
  • You get 5 credit points (lecture + exercises)
  • You need 50% of the points from the exercises to be allowed to register for the exam.
  • Bachelor and Master students are eligible (required lecture (one of the following): The Elements of Statistical Learning I, Elements of Statistical Learning, or Machine Learning)

Course overview

Recent advances in high-throughput technologies have led to an exponential increase in biological data (such as genomic, epigenomic and proteomic data). To find meaningful insights in such large data collections, efficient statistical learning methods are needed. This lecture will give an overview of different statistical learning techniques (LMMs, kernel methods, Graphical Models, Deep Belief Nets) applied to different omics data (e.g., genomics, proteomics, and epigenomics).

Course material

Recommended books:
  • Machine Learning a Probabilistic Perspective by Kevin Murphy
  • Probabilistic Graphical Models, Principles and Techniques by Daphne Koller and Nir Friedman
  • The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Course material is available here: password protected area.