Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores the significance of randomization in protein mass spectrometry and proteomics, highlighting its role in minimizing bias and ensuring research validity.