Lectures related to Coin Change Problem

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

Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.

Optimization algorithms

Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.

Variance Reduction: Strategies and Applications

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.

Multi-arm Bandits

Discusses algorithms for balancing exploration and exploitation in decision-making processes.

Algorithms: Summary of the week

Covers algorithms for searching, sorting, optimization, and the Halting Problem.

Solving Parity Games in Practice

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

Coin Rendering: Part 1

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

Optimization Methods: Theory Discussion

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

Dynamic Programming: Bellman-Ford and Dijkstra

Explores dynamic programming with Bellman-Ford, Dijkstra, greedy strategies, and activity scheduling problems.

Search Algorithms: Abductive Reasoning

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

Computer Architecture: Algorithms to Programs (Compilation)

Explores the transition from algorithms to programs through compilation, emphasizing constraints and machine-understandable coding practices.

Introduction to Algorithms

Explores the ingredients and selection of algorithms for different goals.

Introduction to Algorithms: Course Overview and Basics

Introduces the CS-250 Algorithms course, covering its structure, objectives, and key topics in algorithmic problem-solving.

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Dynamic Programming: Introduction and Fibonacci Numbers

Introduces Dynamic Programming, focusing on saving computation by remembering previous calculations and applying it to solve optimization problems efficiently.

Linear Programming: Solving LPs

Covers the process of solving Linear Programs (LPs) using the simplex method.

Greedy Algorithms & Matroids

Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.

Optimization: Mathematical Principles and Algorithms

Covers mathematical principles and algorithms of optimization, using real-world examples and Python implementation.

Dynamic Programming: How Many Ways to Make Change

Demonstrates dynamic programming to find the number of ways to make change using different coin denominations.