A 6-part series of hands-on Jupyter notebooks covering advanced data science topics including unsupervised learning, probabilistic modeling, neural networks, CNNs, sequence models, and transformers. The sequel to Foundations of Data Science.
A 6-part series of hands-on Jupyter notebooks that progressively teaches data science from Python fundamentals through advanced machine learning models including regression, classification, and ensemble methods.
An 8-week structured curriculum introducing fellows and students to AI Safety and Alignment. Covers machine learning foundations, alignment challenges, interpretability, control methods, red-teaming, and governance.