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Big Data (Summerschool)
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.
Target Group
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.
Learning Objectives
- 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.
Face to face
1 week, fulltime