Lectures related to Latent Tree Learning

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.

Explores group membership and view synchrony in distributed systems, ensuring accurate knowledge and coordinated views.

Covers counting distinct elements using hash functions and the median trick.

Explores total order broadcast and its equivalence to consensus in reliable systems.

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

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.

Continues the discussion on Simon's hidden subgroup problem, focusing on finding a basis.

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

Covers search algorithms, focusing on abductive reasoning and heuristic search strategies.

Explores reliable broadcast in distributed systems, presenting specifications, algorithms, and properties for different types of broadcast.

Explores the hidden sub-group problem and Simon algorithm in quantum computing.

Explains the winning strategy in Connect Four by checking for winning combinations.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Covers the LLL algorithm for reducing lattice bases to shorter and more orthogonal forms through iterative transformations.

Covers the Hidden Subgroup Algorithm in quantum computing, emphasizing projectors and measurement postulates.

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.