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Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores mapping non-linear data to higher dimensions using SVM and covers polynomial feature expansion, regularization, noise implications, and curve-fitting methods.
Discusses the fundamental properties and characteristics of optical detectors, including responsivity, quantum efficiency, and the impact of noise on detection limits.
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.
