Lectures related to Introduction to Category Theory

Covers the basics of categories and functors, exploring properties, composition, and uniqueness in category theory.

Explores examples of categories, morphisms, groupoids, and functors in category theory.

Covers the definition of a group, properties of symmetries, and group operations.

Explores building categories from graphs and the encoding of information by functors.

Explores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.

Introduces the concept of categories essential for understanding group theory.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Covers the fundamentals of integer programming and network flows in directed graphs.

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Covers the independence polynomial of a dependency graph and related concepts such as graph coloring and directed graph properties.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Introduces categories, concrete examples, opposite categories, and isomorphisms, leading to groupoids.

Covers the fundamentals of connectivity in graph theory, including paths, cycles, and spanning trees.

Explores ecological vision and computational agent design in the context of visual perception and behavior.

Covers the concept of maximum flow-minimum cut in directed graphs with capacity constraints.

Covers the fundamental notion of composition in directed graphs and provides examples of their representation.

Covers topics like DFS output, edge classification, acyclic graphs, correctness, time analysis, SCCs, and the Topological Sort algorithm.

Explores the properties of undirected and directed graphs, emphasizing connectivity and network topology modeling.

Explores model selection challenges in causal models and the impact of local geometry on statistical inference.

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.