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 impact of model complexity on prediction quality through the bias-variance trade-off, emphasizing the need to balance bias and variance for optimal performance.
