Dynamic Programming Algorithms Master Course

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Instructions

Chapter 2: [Optional] Setting Up Coding Environment

Lecture 1: Sublime Setup

Lecture 2: Add Master Header File

Lecture 3: Escaping Online Judges

Lecture 4: Common Code Snippets

Lecture 5: Using Macros

Lecture 6: Example Code

Chapter 3: Basics of Recusion

Lecture 1: Recursion Introduction

Lecture 2: Factorial

Lecture 3: Fibonacci

Lecture 4: Sorted Array

Lecture 5: Increasing Decreasing Number

Lecture 6: Power Function

Lecture 7: Power Function Fast

Lecture 8: Tiling Problem

Lecture 9: Count Strings

Lecture 10: Friends Pairing Problem

Lecture 11: Tower Of Hanoi

Lecture 12: Tower Of Hanoi (code)

Chapter 4: Backtracking

Lecture 1: Backtracking Introduction

Lecture 2: Finding Subsets

Lecture 3: Finding Subsets Code

Lecture 4: N-Queen

Lecture 5: N-Queen Backtracking Code

Lecture 6: N-Queen Count Ways

Lecture 7: Generate Brackets

Lecture 8: Generate Brackets Code

Lecture 9: Sudoku Solver

Lecture 10: Suodoku Code

Lecture 11: Hamiltonian Paths Backtracking

Lecture 12: Hamiltonian Paths Backtracking (code)

Lecture 13: Assignments

Chapter 5: Introduction to Dynamic Programming

Lecture 1: Terms and Definitions

Lecture 2: Tabulation vs Memoisation

Lecture 3: Recursion is Everywhere – I

Lecture 4: Recursion is Everywhere – II

Lecture 5: Big TIP

Chapter 6: 1-D Dynammic Programming

Lecture 1: Introduction

Lecture 2: SUPW (Zonal Computing Olympiad)

Lecture 3: SUPW (code)

Lecture 4: IPL

Lecture 5: Min steps to reach one

Lecture 6: Alphacode

Lecture 7: Alphacode (code)

Lecture 8: Rod Cutting

Lecture 9: Rod Cutting (code)

Lecture 10: House Robber

Lecture 11: Palindromic Partioning

Lecture 12: Assignments

Chapter 7: 2-D DP Deep Dive : SubSet Sum Problem

Lecture 1: Subset Sum (problem)

Lecture 2: Subset sum (code)

Lecture 3: Subset Sum with Repeating Numbers

Lecture 4: Subset Sum Repeating Numbers (code)

Lecture 5: Subset Sum (Tabulation)

Lecture 6: Subset Sum Tabulation (code)

Lecture 7: Memory Optimisation – Subset Sum

Lecture 8: Memory Super Optimisation – Subset Sum

Lecture 9: Tracing Back Solutions

Lecture 10: Modulo Sum (codeforces)

Lecture 11: Assignments

Chapter 8: 2D DP: Deep Dive Knapsack Problem

Lecture 1: Introduction and greedy approaches

Lecture 2: 0/1 Knapsack

Lecture 3: Knapsack (code)

Lecture 4: 0/N Knapsack

Lecture 5: Colourful Knapsack

Chapter 9: 2D DP problems

Lecture 1: Paint House

Lecture 2: Paint House (code)

Lecture 3: Paint House(follow up)

Lecture 4: Make Fence Great Again (codeforces)

Lecture 5: Assignments

Chapter 10: Partition Problems

Lecture 1: Plates

Lecture 2: Plates (code)

Lecture 3: Best Time to Buy and Sell Stock

Lecture 4: Best Time to Buy and Sell Stock (code)

Lecture 5: Partition Array for Maximum Sum

Lecture 6: Partition Equal Sum Subset

Lecture 7: Split Array largest Sum

Lecture 8: Split Array Largest Sum (code)

Lecture 9: Palindromic Partitioning 2

Lecture 10: Palindromic Partitioning 2 (code)

Lecture 11: Assignments

Chapter 11: Combinatorics and Dynammic Programming

Lecture 1: Tiling Problem – 1

Lecture 2: Tiling Problem – 2

Lecture 3: Tiling problem – 3

Lecture 4: Tiling Problem – 3 (code)

Lecture 5: Advance Tiling problem (SPOJ – M3TILE) with code

Lecture 6: Number of jumps to reach n

Lecture 7: Binomial Coefficients