Lectures related to Dynamic Programming: Bellman-Ford and Dijkstra

Dijkstra's Algorithm and Probabilistic Analysis

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

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

Covers finding the shortest path in directed graphs efficiently using algorithmic approaches and discussing related NP-complete problems.

Solving Parity Games in Practice

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

Search Algorithms: Abductive Reasoning

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

Coin Change Problem

Explores the coin change problem, comparing greedy and dynamic programming algorithms for optimal solutions.

Dijkstra's Algorithm and Shortest Path

Covers Dijkstra's algorithm for shortest path problems and its application in ALL-TO-ONE and ALL-PAIRS algorithms.

Minimum Spanning Trees: Prim's Algorithm

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

Dynamic Programming: Pascal's Triangle & Floyd's Algorithm

Explores dynamic programming through Pascal's Triangle and Floyd's Algorithm.

Optimization Methods: Theory Discussion

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

Simplex Algorithm: Basics

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Set Cover: Integrality Gap

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

Suspension Optimisation: Performance Analysis and Optimization

Explores the design and optimization of car suspensions for improved performance.

PCA: Derivation and Optimization

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Quantum Approximate Optimization Algorithm

Covers the Quantum Approximate Optimization Algorithm, physically inspired unitary coupled cluster ansatz, hardware-efficient ansatz, and variational quantum eigensolver.

Shortest Path: Properties of the Algorithm

Explains the properties of the shortest path algorithm and how to find the shortest path in a network.

Building Robust Ensembles via Margin Boosting

Delves into building robust ensembles through margin boosting for improved adversarial defense in machine learning models.

Complex Systems: Critical Phenomena

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

Approximation Algorithms

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Monte Carlo Chain: Motivation and Algorithm

Explores the motivation and algorithm behind the Monte Carlo Chain method.