NET DEC (2021) - BFS

Duration: 2 min

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Module outline

  1. About Course: About the Course
  2. General Aptitude & Reasoning: Number System, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Time and Work, Speed Time and Distance, Mixture and Alligation, Data Interpretation, Algebra, Probability, Permutation and Combination, Venn Diagram, HCF LCM, Divisibility Rules, Age Problems, Pipe and Cistern, Partnership, Linear Algebra, Sequence and Series (Series), Statistics, Powers and Exponents (Surds and Indices), Arithmetic, Mensuration and Geometry, Calculus, Trigonometry, Vectors, Speed Mathematics and Basic Mathematics, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Analogy, Series (Number and Letter Series) (Numerical Relations and Reasoning), Puzzle Solving (Puzzles), Syllogisms, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Calendar, Seating Arrangements, Coding Decoding, Blood Relations, Directions (Direction Test), Clock, Analytical Reasoning (Counting Figures Reasoning), Ranking, Order and Sequence, Logical Sequence Of Words, Inequality, Data Sufficiency, Cubes & Dices
  3. Computer Networks & Cybersecurity: 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
  4. Programming, Data Structures & Algorithms: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, OOPS with C++, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  5. AI, Machine Learning, Big Data & Cloud: Emerging Tech Trends, Artificial Intelligence, Big Data Systems, Approaches to AI, Search Algorithms, Knowledge Representation, Cloud Computing
  6. Digital Logic & Computer Organization: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer
  7. TOC & Compiler: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory, Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  8. Discrete Mathematics: Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic
  9. Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS, Linux OS, Distributed Systems
  10. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Object Oriented Technology, HTML Basics, JavaScript-Basics, Java, XML
  11. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, Data Warehousing & Mining, Enhanced Data Models, NoSQL
  12. Other Recurring CS Topics: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, Multimedia
  13. Live Classes: ISRO Live classes
  14. Full Mock Test:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a GATE NET DEC 2021 question on search algorithms in artificial intelligence, asking which statements are false regarding best-first search, A*, recursive best-first search, and the admissibility of h(n) = 0 for the 8-puzzle. The instructor evaluates each option, noting that statement A is false because best-first search is not guaranteed to be optimal despite often being efficient. Statement B is true, as A* is complete and optimal when h(n) is admissible or consistent. Statement C is false, as recursive best-first search has poor space complexity but efficient time complexity—though the instructor clarifies this is a common misconception. Statement D claims h(n) = 0 is admissible for the 8-puzzle, which is true because it never overestimates the cost to reach the goal. The instructor uses handwritten annotations on a digital whiteboard, highlighting 'h(n)=0 → admissible' and emphasizing that h(n) = 0 is a valid heuristic. The correct answer, as indicated in the question, is 'a) A and D only', but the instructor correctly identifies that both A and D are false, with D being true—indicating a possible error in the provided answer key. The teaching progression focuses on evaluating algorithmic properties such as optimality, completeness, and admissibility through direct analysis of each statement.

Chapters

  1. 0:00 – 1:34 00:00-01:34

    The video presents a GATE exam question (NET DEC 2021) on search algorithms in artificial intelligence, asking which statements are false. The question evaluates four claims: A) Best-first search is not optimal but efficient; B) A* is complete and optimal if h(n) is admissible or consistent; C) Recursive best-first search has good time complexity but poor space complexity; D) h(n) = 0 is an admissible heuristic for the 8-puzzle. The instructor analyzes each option, with handwritten annotations emphasizing that h(n) = 0 is admissible because it never overestimates the cost to reach the goal, and this makes statement D true. The instructor also notes that best-first search is not optimal (making A false), and recursive best-first search has poor space complexity, so C is true. The correct answer is determined to be A and D only, as both are false statements.

This segment teaches the evaluation of search algorithm properties—optimality, completeness, and admissibility—through a multiple-choice question. The instructor analyzes each statement: A is false because best-first search lacks optimality; B is true as A* satisfies completeness and optimality with admissible heuristics; C is false due to incorrect complexity claims about recursive best-first search; D is true because h(n) = 0 never overestimates cost, making it admissible. Handwritten annotations reinforce key concepts like 'h(n)=0 → admissible'. The lesson resolves doubts about algorithm correctness, particularly around heuristic validity and the conditions under which search methods guarantee optimal solutions.

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