Germany, Würzburg

Machine Learning and Artificial Intelligence in Biology

when 23 September 2019 - 27 September 2019
language English
duration 1 week
credits 5 EC
fee EUR 400

Modern methods in the life sciences, such as high-resolution microscopy and high-throughput sequencing, generate enormous amounts of data. To discover information in these data and to ask the right questions, new methods from data mining, artificial intelligence and deep learning need to be developed and applied, complemented by theoretical models in an integrated systems approach.

In this summer school, you will learn how to apply cutting edge techniques from artifical intelligence and deep learning for the analysis of biological data (e.g. images, genomes), how to integrate the results with other biological "big" data from genomes to ecosystems, and how to combine data science and quantitative modeling. Local researchers with expertise in bioimage analysis, modeling and machine learning will teach courses together with invited speakers.

Information and application: https://go.uniwue.de/aibio2019

Course leader

Prof. Dr. Philip Kollmannsberger

Target group

This summer school is targeted towards students at the BSc. and MSc. level with a biological or biomedical background. Previous experience with programming and machine learning is helpful, but not necessary.

Course aim

After this five-day intensive course, you will have gained basic skills in machine learning and AI and will be able to apply them to typical data analysis problems in biology and life sciences.

Credits info

5 EC
In order to receive a certificate for the 5 credit points, a written homework has to be submitted by the end of the winter term 2019/20.

Fee info

EUR 400: The participation fee of 400 € includes accommodation in the Babelfish hostel for 5 nights, all lunches and coffee breaks, a raft tour on the Altmain, a conference dinner, and public transport within the city of Würzburg.
EUR 290: A reduced fee of 290 € applies for participants who organize their own accommodation.