compgen2021 hands-on course on machine learning for genomics
Course url: https://compgen.mdc-berlin.de/ or https://bioinformatics.mdc-berlin.de/compgen/2021/
Organizer: Altuna Akalin (https://bioinformatics.mdc-berlin.de/ + http://al2na.co)
Prerequisites
- Programming with R
- Being able to make reports with Rmarkdown
- Understanding of the basic probability and statistics concepts
- Conceptual understanding of high-throughput assays (sequencing, microarrays etc.) in genomics
Learning Objectives and material
Module 1: Statistics for genomics (2-8 August 2021)
- A simple intro to statistical distributions
- hypothesis testing
- linear models.
reading: http://compgenomr.github.io/book/stats.html
slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf
exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/
Module 2: Unsupervised learning for genomics (9-15 August 2021)
- Understanding basic intuition behind machine learning approaches.
- Using unsupervised learning to cluster and visualise data points
- Dimension reduction techniques for visualisation and as input to clustering methods
reading: http://compgenomr.github.io/book/unsupervisedLearning.html
slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf
exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/
Module 3: Supervised learning for genomics (16-22 August 2021)
- Understanding and using supervised learning methods for predictive purposes
- How to measure prediction performance
- Understand and use cross-validation and related concepts
reading: http://compgenomr.github.io/book/supervisedLearning.html
slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf
exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/