This course aims to introduce the core principles and biomedical applications of different data mining and machine learning techniques in a hands-on manner. It will tackle both unsupervised (clustering, frequent pattern mining, data projection) and supervised (classification) techniques. The methods that will be seen include hierarchical clustering, k- means clustering, item set mining, association rule mining, principle component analysis, support vector machines, random forests, bayesian networks and artificial neural networks. Attendees will be introduced to the basic operations of these data mining techniques, with a focus on the practical use and interpretation of these procedures rather than the mathematical formulas. In addition, attendants will be introduced to some important data processing and performance evaluation methods related to these data mining techniques. The software used in this course will be R. The course itself will consist of 50% theory lessons, 40% hands-on practicals and 10% application case studies.

Organised by biomina:

Dr. Pieter Meysman
Prof. dr. Kris Laukens