Lectures related to Algorithmes: introduction

Graph Algorithms II: Traversal and Paths

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

Theory of Computability: Solvability and Complexity

Explores the theory of computability, decision problems, complexity classes, and the 'P vs. NP' conundrum.

Complexity Classes: Introduction and Examples MOOC: Information, Computation, Communication: Introduction to computational thinking

Introduces complexity classes, including P and NP, and explores examples of easy and hard problems.

Communication Amid Uncertainty

Explores challenges in human communication, context importance, compression problems, uncertain functionality, and proof verification.

Graph Algorithms: Modeling and Traversal

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

Stein Algorithm: Polynomial Identity Testing

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Complexity & Induction: Algorithms & Proofs

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

Promise Constraint Satisfaction and Width

Covers Promise Constraint Satisfaction Problems complexity, width, graph coloring, polymorphisms, and algorithms.

Dynamic Programming: Shortest Paths Algorithms

Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.

Elements of Computational Complexity

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

Complexity of Algorithms

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Complexity Classes: P and NP

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.

Distributed Quantum Computing

Explores communication complexity and quantum computation models in Distributed Quantum Computing.

Elements of computational complexity Log in to Mediaspace to watch this video

Covers classical and quantum computational complexity concepts and implications.

Dynamic Programming: Knapsack Log in to Mediaspace to watch this video

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Introduction to Information, Computation, and Communication Log in to Mediaspace to watch this video

Introduces the fundamental principles of Information, Computation, and Communication theory, covering genomics, medical imaging, and assistive technology.

Theory of Computation: Decidability and Complexity Log in to Mediaspace to watch this video

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Graph Theory and Network Flows Log in to Mediaspace to watch this video

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Statistical Analysis of Network Data: Structures and Models Log in to Mediaspace to watch this video

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