Practice Question

Duration: 7 min

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Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Ability: Percentage, Profit and Loss, Ratio and Proportion (Ratios), Average, Time and Work, Speed Time and Distance, Simple Interest and Compound Interest, Age Problems, Number System, Divisibility Rules, HCF LCM, Algebra, Permutation and Combination, Probability, Data Interpretation, Logarithm, Sequence and Series (Series), Powers and Exponents (Surds and Indices), Speed Mathematics and Basic Mathematics
  2. Logical Reasoning: Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Data Sufficiency, Venn Diagram, Inequality, Series (Number and Letter Series) (Numerical Relations and Reasoning), Logical Sequence Of Words, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Analogy, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Subject Verb Agreement (Verb Noun Agreement), Tenses, Articles, Preposition, Conjunction, Modals, Active Passive Voice (Voice), Direct and Indirect Speech, Vocabulary, Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Completion (Fill in the blanks), Sentence Correction (Error Correction), Cloze Test (Cloze Passage), Facts, Inference and Judgements
  4. Programming Fundamentals (Technical): C Fundamentals
  5. Data Structures (Technical): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  6. Algorithms (Technical): Algorithm Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Time Complexity Analysis, Advanced Algorithms
  7. OOPs (Technical): Object Oriented Programming (OOPS)
  8. DBMS (Technical): Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, ORDBMS, Database Security & Authorization, Query Processing & Optimization, Enhanced Data Models, Data Warehousing & Mining, Big Data Systems, NoSQL
  9. SQL (Technical): SQL
  10. Operating Systems (Technical): Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Windows OS, Linux OS, Security, Distributed Systems, Virtual Machines
  11. Computer Networks (Technical): Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security, Mobile Technology, Cloud Computing and IoT, Cyber and Info Security
  12. Software Engineering (Technical): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  13. Coding/Automata Round: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir, Pseudo Codes
  14. Personality / Behavioral Assessment: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
  15. Writing Assessment: English Writing Ability
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video demonstrates the application of the Huffman coding algorithm to a set of characters with given probabilities. The instructor guides viewers through the process of generating a Huffman tree, deriving binary codes for each character, and calculating the average number of bits required per character. The lesson covers the step-by-step combination of nodes with the lowest probabilities, the assignment of binary values to tree branches, and the final computation of the weighted path length to determine encoding efficiency.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The lecture begins with the problem statement: "Consider the following character with probability and generate Huffman tree, find Huffman code for each character, find the number of bits required per character?" A table is displayed showing five characters (M1 to M5) with probabilities .12, .04, .45, .17, and .23 respectively. The instructor starts the Huffman algorithm by identifying the two characters with the lowest probabilities, which are M2 (.04) and M1 (.12). He crosses them out and combines them into a new internal node with a probability of 0.16 (0.04 + 0.12). He draws the first part of the tree structure on the whiteboard, showing the parent node 0.16 branching down to M2 and M1. This establishes the foundation for the tree construction.

  2. 2:00 – 5:00 02:00-05:00

    The instructor proceeds to the next step of the algorithm. The remaining probabilities are M3 (.45), M4 (.17), M5 (.23), and the new node (0.16). He sorts these and identifies the two smallest: the node (0.16) and M4 (.17). He combines these to form a new node with probability 0.33. Next, he combines M5 (.23) with the 0.33 node to create a 0.56 node. Finally, he combines M3 (.45) with the 0.56 node to reach the root with probability 1.0. He then assigns binary codes to the branches, marking left branches as 0 and right branches as 1. This results in specific codes: M3 gets '0', M5 gets '10', M4 gets '111', M1 gets '1101', and M2 gets '1100'. The tree is now fully constructed with all leaves labeled.

  3. 5:00 – 7:19 05:00-07:19

    The final phase involves calculating the average number of bits required per character. The instructor writes down the length of the code for each character: M1 has 4 bits, M2 has 4 bits, M3 has 1 bit, M4 has 3 bits, and M5 has 2 bits. He sets up the calculation for the weighted path length: (4 * 0.12) + (4 * 0.04) + (1 * 0.45) + (3 * 0.17) + (2 * 0.23). He computes each term: 0.48, 0.16, 0.45, 0.51, and 0.46. Summing these values, he arrives at a total of 2.06 bits per character, which he writes as the final answer for the average bits required. This calculation demonstrates the efficiency of the Huffman code.

The video effectively connects the theoretical steps of Huffman coding to a practical calculation. By building the tree from the bottom up and assigning codes based on branch paths, the instructor shows how frequent characters get shorter codes. The final calculation of 2.06 bits per character quantifies the compression efficiency achieved by this specific encoding scheme.

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