Theory of Computation Lectures | Computer Science and Computer Engineering

Greedy fails. DP wins. Here's why. Lecture link: https://www.youtube.com/watch?v=Xu5iBfS0M6I In this video, we solve the Coin Change Problem — one of the most important problems in computer science. We first show how the greedy approach (always pick the biggest coin) gives the WRONG answer, then build the Dynamic Programming solution step by step from scratch. 📌 What you'll learn: → Why greedy breaks on certain coin sets → The DP recurrence: dp[i] = min(dp[i], dp[i - coin] + 1) → How to fill the DP table manually, one cell at a time → Time & space complexity analysis 🔔 Subscribe to follow the full Optimization Problems series! #dynamicprogramming #CoinChange #algorithms #problemsolving #codinginterview #computerscience #programming #greedyalgorithm #leetcode

Lecture Link In this lecture, we apply the Fractional Knapsack Algorithm to a real-world scenario — a store owner trying to display the most profitable fruits on a limited-weight table. 🏪🍎 You’ll learn how the greedy method works, why it’s optimal for divisible items, and how to calculate the maximum total value using value per kilogram. We’ll go through the full reasoning: 1-Defining the problem and scenario 2-Computing value densities for each fruit 3-Filling the 30 kg capacity step by step 4-Showing exactly how much of each fruit to take 5-Calculating the final maximum profit = $222 This example will help you understand how greedy algorithms make optimal decisions for fractional knapsack problems and how to reason through each step manually.

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.

Lecture Link In this video, we break down regular expressions (regex) step by step — from the basic building blocks to how they form regular languages. You’ll learn: 1- The three core operations: Concatenation, Union, and Kleene Star 2- The difference between the empty string (ε) and the empty set (∅) 3- The distinction between Kleene Star (★) and the plus (+) operator 4- How regex compares to arithmetic operations with simple analogies 5- Solved examples to help you actually use these operations By the end, you’ll understand how regular expressions are built, how they define regular languages, and why they’re such a powerful tool in computer science. If you’re a student, developer, or just curious about how search patterns and compilers work, this video is for you!

  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.

System Design Decision Flow 1-  Clarify Requirements    • Functional: What features are needed?      – Example: Store, retrieve, update user profiles; search by email, etc.    • Non-functional: Scale, performance, reliability?     – Example: 100M users, 1000 RPS, 99.99 2-  High-Level Components 2.1- Client         The entry point for user interaction via web or mobile interfaces. Clients send HTTP or API requests to the backend through gateways or load balancers. 2.2- CDN (Content Delivery Network)           A CDN is a globally distributed network of edge servers that caches and serves static content (like images, CSS, JS, fonts, and videos) from locations geographically closer to users. This reduces latency, offloads traffic from your origin server, and improves content delivery speed. Use when: • You serve static assets to a global audience. • You want to reduce load on your a...

In this video, we solve a popular SQL problem from LeetCode that asks: "Find all days with higher temperatures than the previous day." We walk through the structure of the Weather table, explain the problem with an example, and then dive into the SQL solution using a self join and date manipulation . Perfect for SQL beginners and anyone preparing for data science or software engineering interviews! 📘 Topics covered: SQL self join Working with dates using DATE_ADD Real-world SQL interview question Step-by-step explanation of the logic Whether you're brushing up on your SQL skills or practicing for your next coding interview, this problem is a great way to test your understanding of comparing rows across time. leetcode,sql,leetcode_sql,datascience,codinginterview,softwareengineer,sqltutorial,sqlpractice,sqlinterviewquestions,leetcodesolutions,selfjoin,datefunctions,sqlbeginner,programming,learnsql,techcareer,dataanalytics,sqlproblems,sqlqueries

Introduction: Are you curious about how websites work? Wondering what makes a website both beautiful and functional? In my latest YouTube video, I break down the difference between frontend and backend development using a relatable home analogy. Whether you’re new to coding or looking to refresh your knowledge, this video is the perfect starting point for your web development journey! What You’ll Learn in the Video: Frontend Development : Learn about the tools like HTML, CSS, and JavaScript that create the visible and interactive parts of a website. Understand how each tool contributes to building a seamless user interface. Backend Development : Discover how frameworks like Django handle data processing, user authentication, and more behind the scenes. See how the backend ensures everything works smoothly. How Frontend and Backend Work Together : Understand why websites need both frontend and backend to function. Explore a practical analogy to make the concept easier to grasp. Than...

In this video, we dive into the fundamentals of designing a finite automaton. Whether you're a student or just curious about computational theory, this guide will help you understand how to create a finite automaton step-by-step. 🔹 What You'll Learn: The basics of finite automata and their purpose. How to design states and transitions for your machine. A practical example of creating an automaton that recognizes strings with an odd number of '1's. We’ll break down the process into easy-to-follow steps, from conceptualizing your machine to writing transition diagrams and defining states. This video is perfect for beginners and anyone looking to sharpen their understanding of finite automata.

Demystifying the Halting Problem: Turing's Unsolvable Puzzle 🧩 Hello everyone, welcome to my channel! Today, we explore one of the most profound topics in theoretical computer science: the Halting Problem . What you'll learn in this video: Introduction to the Halting Problem and its significance Alan Turing's proof by contradiction that the Halting Problem is unsolvable Step-by-step explanation of the proof's logic and implications We'll break down Turing's ingenious proof, starting with the assumption of a reliable program (R) that can determine if another program halts. Then, we'll build on this idea to show why such a program cannot exist through a clever contradiction involving a larger machine (N). If you've ever been curious about the limits of computation and the genius of Alan Turing, this video is a must-watch! We'll simplify complex concepts to help you grasp the core ideas and appreciate the depth of Turing's work. Don't miss out...

🚀 Hey everyone, welcome to my channel! Today, we dive deep into the fascinating world of theoretical computer science. We'll unravel the mysteries of the Pumping Lemma and show you how to use it to prove that a language is not regular. What you'll learn in this video: Understanding the basics of regular languages Key properties of regular languages and their closure properties The significance of the Pumping Lemma in proving non-regular languages Step-by-step example of using the Pumping Lemma to prove a language is non-regular If you've ever been puzzled by finite state machines or wondered how to prove a language isn't regular, this video is for you! We'll break down complex concepts into easy-to-understand steps. Don't miss out on this essential topic in computer science! If you enjoyed this video, please like, subscribe, and share it with your friends. Your support helps us create more valuable content. See you in the next lecture! 🔔 Subscribe for more ...

#finiteautomata #theoryofcomputation #nfa #dfa #automatatheory #computerengineering #cslectures #cenglectures #computerscience #cs #lecture #lecture_series #FormalLanguages #education #techlearning #mechanicalengineering #tutorial #science What you'll learn: 1- The 5-tuple representation of DFA and NFA 2- Detailed examples to illustrate the concepts 3- How to define the transition function, initial state, and accepting states Whether you're a computer science student, a tech enthusiast, or just curious about automata theory, this video is for you! Don't forget to like, comment, and subscribe for more insightful lectures.

#automatatheory #theoryofcomputation #finitestatemachines #computerscience #computerengineering #mechanicalengineering #dfa #nfa #deterministic #nondeterministic #determinism #cs #theoryofcomputation #LearningWithVisuals #educationalvideo #education #cslectures Welcome to our in-depth tutorial on Finite State Machines (FSM), designed to help you master the concepts of NFAs (Nondeterministic Finite Automata) and DFAs (Deterministic Finite Automata). Whether you're a student, a professional, or just curious about computational theory, this video breaks down complex topics into easy-to-understand explanations with visual aids. 📚 What You'll Learn: Finite State Machines (FSM): Understand the basic principles and components of FSMs. 1- NFA (Nondeterministic Finite Automaton): Learn how NFAs work, including the concept of ε-transitions and how they differ from DFAs. 2- DFA (Deterministic Finite Automaton): Discover the deterministic nature of DFAs and their...

#automatatheory #cenglectures #computerengineering #computerscience #cs #cslectures #education #language #maths #theoryofcomputation #university #universitylecture Welcome to the first lecture of the Theory of Computation series! In this video, we delve into the foundational concepts of theoretical computer science. Here's what we'll cover: What is a Language? Understand the definition of a language in the context of computer science and its significance. What is a String? Learn about strings, their structure, and how they form the basis of languages. Examples of Languages and Strings See practical examples to illustrate these concepts clearly. Operations on Languages Explore the fundamental operations that can be performed on languages, including: Union Intersection Concatenation Kleene Star Introduction to Regular Languages Get a brief overview of regular languages and their importance in the theory of computation.