Explores the structured approach to exploratory spatial data analysis, emphasizing the importance of analytical frameworks and the Visual Seeking Mantra.
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Introduces Geographically Weighted Regression, a spatially explicit approach to measure relationships between variables with location-specific outputs.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Delves into the relationships between mood disorders, cognitive performance, and brain plasticity in urban environments, using data from medical cohorts.
