Kernel Regression: Weighted Average and Feature Maps
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Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.
