Statistical Learning in Computational Biology

Statistical Learning in Computational Biology

News

  • 2016-07-21 There will still be a lecture on the 28th.
  • 2016-06-27 The next tutorial will be at the 7th of June from 8:30 - 10 in room 21 at building E1.4 (MPI for Informatics building).
  • 2016-06-23 The new assignment is online in the password protected area.
  • 2016-06-09 No lecture on the 16th of June! Lecture is postponed to the 17th of June from 12:30 p.m. to 2 p.m. in the usual room (room 001 in building E2.1).
  • 2016-06-02 The third tutorial will be on the 16th of June (Thursday) at 8:30 a.m. in room 533 at the MPI for Informatics (E1.4).
  • 2016-05-20 The second tutorial will take place on the 25th of May at 8:30 a.m. in room 023 at the MPI for Informatics (E1.4).
  • 2016-05-19 The second assignment can be found in the password protected area.
  • 2016-04-28 The first assignment can be found in the password protected area.
  • 2016-04-28 The first tutorial will be on the 4th of May at 9:00 a.m. in room 023 at the MPI for Informatics (E1.4).

General info

  • Lecturer: Nico Pfeifer
  • Tutor: Anna Feldmann
  • Lecture: Thursdays from 10:00 - 12:00 at room 001 in building E2.1 (first lecture will be on the 21st of April)
  • Tutorial (every other week): date will be determined in first lecture.
  • 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.