Theory of Computation Lectures | Computer Science and Computer Engineering

  Lecture Link In this lecture, we introduce the Fractional Knapsack Problem, one of the most important applications of the Greedy Algorithm in optimization. You’ll learn why we use it, where it applies in the real world, and how to design the algorithm step by step. We’ll walk through the intuitive idea behind value-per-weight (density), explore practical examples like resource allocation and profit maximization, and write the pseudoalgorithm you’ll use to solve problems in the next lecture. ⚡️ In the next video, we’ll solve full examples step by step, so make sure you watch this one first to master the concept!

  Lecture Link In this video, we’ll write and explain the 0/1 Knapsack algorithm in Python line by line. You’ll learn how to: 1- Build a Dynamic Programming (DP) table to find the maximum profit 2- Understand each part of the nested loops that fill the table 3- Implement the backtrace function to recover the selected items 4- See how DP and backtracking work together in solving optimization problems

Lecture Link In this video, we solve the 0/1 Knapsack Problem step by step using Dynamic Programming . We cover two essential parts of the solution: 1️⃣ Building the DP table — to find the maximum profit achievable without exceeding the knapsack’s capacity. 2️⃣ Backtracking the table — to trace which items were actually chosen to reach that maximum profit. By the end of the lecture, you’ll not only know the optimal profit but also exactly which items to select , making this one of the clearest walkthroughs of the knapsack problem you’ll find.

Lecture Link In this lesson, we tackle the 0/1 Knapsack Problem , a classic optimization problem in computer science and interview prep. We’ll motivate the problem, define it clearly, and then solve it using dynamic programming . You’ll see the pseudoalgorithm , learn why we use a table (columns = capacities 0…W, rows = items), and walk through a concrete example step by step. In the next video, we’ll implement it in Python . What you’ll learn: What the 0/1 Knapsack Problem is used for (budgeting, scheduling, resource allocation, portfolios) The DP intuition: solve small capacities first, reuse results How to build the DP table: rows = items, columns = capacities The key choice at each cell: take or skip the item How to trace back selected items from the final table Clean pseudoalgorithm you can convert to code

Lecture Link From optimization problems to dynamic programming, greedy algorithms, branch and bound, and more , this playlist is designed to help you: Prepare for coding interviews Learn core computer science fundamentals Improve your problem-solving skills Understand algorithms step by step Perfect for students, developers, and anyone who wants to level up their coding and algorithm knowledge .

Lecture Link In this video, you’ll learn: 1- The Chomsky Hierarchy (Regular → Context-Free → Context-Sensitive → Recursively Enumerable → Undecidable) 2- The machines that recognize these languages (Finite Automaton, Pushdown Automaton, Linear Bounded Automaton, Turing Machine) 3- Why understanding this hierarchy is crucial in computer science 4- The Balanced Parentheses problem and why Regular Languages can’t solve it 5- How stack memory makes Context-Free Languages more powerful 6- By the end, you’ll understand the big picture before diving deeper into automata and formal languages.