Educators:
Anup Kumar, Alireza Khanteymoor, Björn Grüning (RBC), Fotis Psomopoulos, Jen Harrow, Vahid Jalili, Jeremy Goecks
Date:
22. - 26.06.2020
Location:
Online
Contents/Learning goals:
This workshop intends to facilitate the training and discourse amongst researchers interested in machine learning using Galaxy. It will be a one-week event, including webinar sessions in which we will introduce machine learning backgrounds and train researchers to use Galaxy for machine learning analysis. Every webinar session will be followed by a self-training day, in which experts will answer questions in a support channel and support on a peer-to-peer basis.
The workshop will include:
- Introduction to the Galaxy data analysis platform
- Machine learning 101
- Regressor (linear, non-linear and ensemble models)
- Classifiers (logistic regression, k-nearest neighbours, support vector machines and random forest)
- Feature selection methods
- Clustering (Hierarchical, K-means and DBSCAN)
- Hyper-parameter optimization
Prerequisites:
The target audience includes researchers interested in analysing scientific data using machine learning algorithms for classification, regression and clustering tasks. Specifically, we encourage scholars that have begun biological data analysis and are interested to know what is machine learning and how they can use Galaxy for machine learning tasks. While the datasets used during this workshop will be biological and chemical; you will learn universal tools, workflows and a framework applicable to other domains.
Keywords:
Galaxy, Machine Learning
Tools:
Galaxy
Contact:
Björn Grüning
Alireza Khanteymoor