Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.
Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.
Covers the background on symplectic geometry, focusing on symplectic manifolds and canonical structures.
Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.
