The use of modern quantitative technologies to characterize complex phenomena represents the standard approach in almost every research domain. Biology makes no exception and the use of multi-omics techniques (metabolomics, transcriptomics, genomics and proteomics) is pervasive in every facet of life sciences. The resulting multivariate datasets are highly complex and advanced data analysis approaches need to be applied to optimize the information retrieved. For relatively large-scale studies, machine learning represents a valid tool to complement classical multivariate statistical methods.
The objective of this course is to highlight advantages and limitations of these data analysis approaches in the context of biological research, providing a broad hands-on introduction to the use of multivariate methods and machine learning for the analysis of ‘omics datasets.

The syllabus has been planned for people with zero or very basic knowledge of machine learning. Students are assumed to have basic familiarity with R programming language.

 Each session consists of a lecture of two hours followed by one hour of practical exercises/demonstration. There will also be plenty of time for students to discuss their own problems and data.

Monday- 2-8 pm Berlin time

   -  General Introduction
Data mining, -omics and machine learning
Experimental Design

    - Jupyter for R
Introduction to advanced R data libraries
Introduction to R frameworks for machine learning

   -  Multivariate data
Generalities
Variable associations and false discoveries

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.