Learn the fundamentals and advanced applications of Dynamic Programming in this comprehensive 3.5-hour tutorial. Explore essential concepts, including the definition and implementation of Dynamic Programming in data structures and algorithms. Dive into practical problem-solving with examples such as the Coin Change Problem, addressing both the number of ways to achieve a total and finding the minimum number of coins required. Tackle the Traveling Salesman Problem and understand its solution using Dynamic Programming. Master the 0/1 Knapsack Problem and compare it with the Fractional Knapsack Problem solved through the Greedy Method. Discover techniques for solving the Subset Sum Problem and uncover the intricacies of finding the Longest Common Subsequence. Enhance your algorithmic skills and problem-solving abilities through this in-depth exploration of Dynamic Programming techniques.
Overview
Syllabus
What is Dynamic Programming | How to use it | Data structures and Algorithms.
Coin Change Problem Number of ways to get total | Dynamic Programming | Algorithms.
Coin Change Problem: Minimum number of coins Dynamic Programming.
Traveling Salesman Problem using Dynamic Programming | DAA.
0/1 knapsack problem-Dynamic Programming | Data structures and algorithms.
Fractional Knapsack Problem using Greedy Method | Example | Data structures and algorithms.
Subset Sum Problem using Dynamic Programming | Data Structures and Algorithms.
Longest Common Subsequence- Dynamic Programming | Data structures and algorithms.
Taught by
Jenny's lectures CS/IT NET&JRF
