Concurrent Programming: Theory to Practice

Related lectures (29)

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Discusses the graph of a relation and the concept of separation.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores the ubiquity of graphs in modern data and analytics, focusing on the shift in organizations' perception of graph technologies.

Explains the relationship between two functions through restriction and prolongation, emphasizing the importance of defining a function by its graph.

Explores causal inference through learning graph structures for causal reasoning from observational data.

Covers cache coherence, sharing data in multiprocessor caches, directories, and coherence protocols.

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Explores Breadth-First Search and Dijkstra's algorithm for finding shortest paths in graphs.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.