The workshop aims to provide a hands-on introduction to important Machine Learning (ML) techniques with the opportunity to discuss their applicability in the context of participants’ data. Biologists with interest in data analysis and little or no prior knowledge of machine learning are encouraged to attend. Basic programming skills (e.g. in Python or R) will be helpful, but are not required.

Topics: Supervised Learning (Support Vector Machine, Decision Tree, Random Forest, Neural Network), Unsupervised Learning (clustering: hierarchical, model-based, similarity-based, consensus), image classification, object detection, feature extraction (PCA)

Location: Botanische Staatssammlung München, Menzinger Straße 67, D-80638 München, Germany, Rm. 109 (follow the signs from the main entrance hall).

Registration: Registration is now closed. Please send last minute requests to christoph.oberprieler@ur.de.

Organization: Christoph Oberprieler (University of Regensburg)

Accommodation: Rooms have been reserved at Hotel Kriemhild, Guntherstraße 16, 80639 München and Amalienburg Hotel, Amalienburgstraße 24-26a, 81247 München

Instructors: Tankred Ott (University of Regensburg), Ulrich Lautenschlager (University of Regensburg)

Catering: Lunch and coffee breaks will be catered on both days

Preliminary schedule

Thursday, 28 November

9:00 - 18:00

• Introduction to Machine Learning (ML) and Artificial Intelligence (AI)

• Setup of the working environment

• Principal Components Analysis (PCA)

• Clustering methods and consensus clustering

• Random Forest (RF)

• Boosted Regression Trees (BRT)

Friday, 29 November

9:00 - 16:00

• Support Vector Machine (SVM)

• Neural Networks (NN)

• Convolutional Neural Networks (CNN)

• Image classification

• Object detection

For questions please contact tankred.ott@ur.de.