Search Algorithms: Abductive Reasoning

Related lectures (28)

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Covers the Simplex algorithm for function minimization with linear constraints.

Covers the formulation of constraint satisfaction problems and systematic algorithms for solving them efficiently.

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Explores uncertain reasoning, Bayesian networks, and stochastic resolution, emphasizing the importance of probabilistic logic and abduction.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

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

Delves into the concept of proof in mathematics, emphasizing the importance of evidence and logical reasoning.

Introduces Dijkstra's algorithm and probabilistic analysis through the Hiring Problem.

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Explores insights into beam search and decoding sequence models in NLP, emphasizing the cognitive motivation behind search algorithms.