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Covers the concept of the exponential family and discusses forward and backward maps, expensive computations, parameters, functions, and convexity.
Explores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.
Explores directed networks with asymmetric relationships and hypergraphs that generalize graphs by allowing edges to connect any subset of nodes.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Covers the concept of affine transformations in functions, including shifting and compressing functions.
