Neural Network: Random Features and Kernel Regression
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Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.
Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.
Explores belief propagation, frozen clusters, and colorability thresholds in graphical models, leading to the significance of survey propagation in solving constraint satisfaction problems.
Explores non-linear SVM using kernels for data separation in higher-dimensional spaces, optimizing training with kernels to avoid explicit transformations.
