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Related lectures (26)

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Graphs: Properties and Representations

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Algorithms: Final Exam Review

Provides a detailed review of the 2016 final exam, covering various algorithmic problems.

Graphical Models: Representing Probabilistic Distributions

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

Graph Algorithms II: Traversal and Paths

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Graph Algorithms: Modeling and Representation

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

Optimal Binary Search Trees

Explains Optimal Binary Search Trees using dynamic programming and covers a midterm exam from 2016.

Optimal Binary Search Tree

Explores optimal binary search trees to minimize expected search cost efficiently.

Graph Algorithms: Basics

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

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Graph Algorithms: Memory Management and Traversal

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.