Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Explores the stationary mean-field concept in computational neuroscience to predict neuronal activity based on population and single neuron firing rates.
Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.
