Robust and Scalable Systems

Related lectures (30)

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

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

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

Covers graph processing with a focus on Oracle Labs PGX, discussing graph analytics, databases, algorithms, and distributed analytics challenges.

Explores concurrent programming theory and practice, covering system models, cache coherence, and graph processing.

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

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

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

Explores scaling pattern matching in large graphs and optimizing query execution under memory constraints using work-sharing and batch processing.

Explores belief propagation in random graphs and Bethe free entropy.