Big Data (Summerschool)

The digital universe is expanding continuously. This huge amount of information is often referred to big data have a huge potential to answer questions that could not be answered before. For example, in biomedical sciences, researchers increasingly make use of these Big Data, by pooling real-time data from multiple sources including electronic healthcare records in order to e.g. detect diseases at an early stage. The summer school on big data will prove you with a sound introduction to this exciting new field in health research. Spanning topics such as:

• Introduction to machine learning (ML)
• Automated ML
• Causal inference using big data and ML
• Natural language processing
• Data linkage

This will be embed in medical research through numerous reall-life examples and case-studies.

• Run and understand basic Python scripts to perform machine learning analyses of large datasets;
• Interpret results of a simple machine learning pipeline, including its design and performance;
• Understand and explain basic concepts of machine learning, including: supervised/unsupervised learning, overfitting, cross-validation, evaluation metrics, bias-variance tradeoffs, and benchmarking;
• Understand and explain basic concepts of deep learning;
• Understand and explain basic concepts of causal modeling;
• List several existing and potential applications of machine learning and AI in medical research.

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

1 week, fulltime (face to face)