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.

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.

• 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.

1 week, fulltime