05 — Links / Resources
A curated list of websites I have found valuable throughout my research, writing, and analysis work.
Supervised Machine Learning for Science
An open online textbook (by Christoph Molnar & Timo Freiesleben) exploring how to apply supervised machine learning in scientific research. Covers the theory and practice of integrating ML not merely as a prediction tool but as a scientific instrument equipped with interpretability, causal reasoning, and uncertainty quantification. Published under CC BY-NC-SA 4.0.
ml-science-book.comInterpretable Machine Learning
A comprehensive guide to making black-box models explainable (by Christoph Molnar). Covers a wide range from interpretable models such as decision trees and linear regression to model-agnostic methods including LIME and SHAP (Shapley values). The third edition (2024) adds new techniques. Accessible without extensive mathematical prerequisites.
christophm.github.io/interpretable-ml-book