This two-day workshop aims at transforming your keen interest into the workings of machine learning algorithms into practical knowledge on how to build accurate predictive models, mainly focussed on classification models.

Objectives

Machine learning has become ubiquitous in biotechnology (as in many other fields), fueled largely by the increasing availability and amount of data. Learning algorithms can figure out how to perform important tasks by generalizing examples. Typical applications are diagnoses/prognoses, gene/protein annotation, drug design, image recognition, text mining and many others. However, building successful machine learning models requires a substantial amount of “black art” that is hard to find in textbooks. This course is an interactive Jupyter Notebook (Python) that will teach you how to build successful machine learning models. No background in machine learning is assumed, just a keen interest.

Required skills

Models are build in the Python programming language so basic knowlegde of Python is a plus.

Trainers

Ralf Gabriels and Robbin Bouwmeester from compomics