Lecture Machine learning in life sciences
Rolf Backofen, Fabrizio Costa
Milad Miladi, Stefan Mautner
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.
The Elements of Statistical Learning (2nd edition) by Hastie, Tibshirani and Friedman (2008). Springer-Verlag.
Lecture/Exercise: Tue 10-12 in building 106, SR 00-007
Lecture/Exercise: Wed 14-16 in building 106, SR 00-007
Written Examination: 30.03.2017 14:00 in Building 101, HS-036
If you had specific questions about the slides, you can send us a limited number of specific questions before the session day. Then we organize a Q-A session one week before the written exam. The session date is 22.03.2016 14:00 in 106-SR00-007.
Lecture slides and exercise sheets
- 18 Oct 2016 Python introduction
- 25 Oct 2016 Introduction, Predictive models
- 26 Oct 2016 Probabilities
- 02 Nov 2016 Bias Variance Decomposition
- 08 Nov 2016 Curse of Dimensionality
- 09 Nov 2016 Exercise_sheet1
- 15 Nov 2016 Filter Methods part 1/2
- 16 Nov 2016 Filter Methods part 2/2
- 22 Nov 2016 Wrapper Methods
- 23 Nov 2016 Exercise_sheet2
- 30 Nov 2016 Exercise_sheet3 (Updated schedule)
- 06 Dec 2016 Exercise_sheet4
- 07 Dec 2016 No lecture/exercise session
- 13 Dec 2016 Embedded Methods (Postponed)
- 14 Dec 2016 Embedded Methods: LASSO part 1/2 (Postponed)
- 20 Dec 2016 Clustering
- 10 Jan 2017 Hierarchical clustering
- 11 Jan 2017 Exercise_sheet5
- 17 Jan 2017 Spectral Clustering
- 18 Jan 2017 Exercise_sheet6
- 24 Jan 2017 BiClustering
- 25 Jan 2017 Exercise_sheet7
- 31 Jan 2017 Dimensionality reduction
- 01 Feb 2017 Exercise_sheet8