Bioinformatics
Institute of Computer Science
University Freiburg
de

Lecture Machine learning in life sciences

Lecturers

Rolf Backofen, Fabrizio Costa

Teaching assistants

Milad Miladi, Stefan Mautner

Introduction

The amount of available biological data is growing at an unprecedented rate. In order to make an effective use of all these data one needs an efficient and practical information management but also and foremost tools for the extraction and distillation of useful and meaningful knowledge. This latter aspect is currently one of the main challenges in computational biology. The aim of this course is to introduce the basic techniques and types of machine learning models employed in modern molecular biology research. The course will maintain a double perspective: from the biological point of view we consider problems in the domains of genomics, proteomics, systems biology and biological literature information mining; from the machine learning point of view, we consider questions such as the underlying assumptions in predictive models, the quality assessment problem, the design choices for supervised and unsupervised models.

Textbook

The Elements of Statistical Learning (2nd edition) by Hastie, Tibshirani and Friedman (2008). Springer-Verlag.

Dates

Lecture slides and exercise sheets

  1. 18 Oct 2016 Python introduction
  2. 25 Oct 2016 Introduction, Predictive models
  3. 26 Oct 2016 Probabilities
  4. 02 Nov 2016 Bias Variance Decomposition
  5. 08 Nov 2016 Curse of Dimensionality
  6. 09 Nov 2016 Exercise_sheet1
  7. 15 Nov 2016 Filter Methods part 1/2
  8. 16 Nov 2016 Filter Methods part 2/2
  9. 22 Nov 2016 Wrapper Methods
  10. 23 Nov 2016 Exercise_sheet2
  11. 30 Nov 2016 Exercise_sheet3 (Updated schedule)
  12. 06 Dec 2016 Exercise_sheet4
  13. 07 Dec 2016 No lecture/exercise session
  14. 13 Dec 2016 Embedded Methods (Postponed)
  15. 14 Dec 2016 Embedded Methods: LASSO part 1/2 (Postponed)
  16. 20 Dec 2016 Clustering
  17. 10 Jan 2017 Hierarchical clustering
  18. 11 Jan 2017 Exercise_sheet5
  19. 17 Jan 2017 Spectral Clustering
  20. 18 Jan 2017 Exercise_sheet6
  21. 24 Jan 2017 BiClustering
  22. 25 Jan 2017 Exercise_sheet7
  23. 31 Jan 2017 Dimensionality reduction
  24. 01 Feb 2017 Exercise_sheet8