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    Principles In Theory Of Computation

    Posted By: ELK1nG
    Principles In Theory Of Computation

    Principles In Theory Of Computation
    Published 12/2023
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 1.49 GB | Duration: 6h 36m

    Automata Theory, Computability Theory, Computational Complexity Theory

    What you'll learn

    Understanding the operation of Finite Automata and Hierarchy of Grammars in solving the problems

    Examining the Grammars and Languages using Pumping Lemma

    Design Pushdown Automata for Computational Logic

    Design Turing Machine for general purpose computer operations

    Evaluate decidability, undecidabilty and Polynomial class of Problems

    Requirements

    Basic Mathematical Knowledge

    Description

    Theoretical Computer Science is a field where all the real world computational problems come under it. Theoretical Computer Science is also called as Theory of Computation. Theory of computation speaks about “How efficiently the real world problems can be solved by using an algorithm in a model of computation. The model of computation denotes any mathematical model which is embedded on any electronic hardware through the software. Theory of computation is divided in to three sub fields. They are automata theory, computability theory and computational complexity theory. Automata theory denotes the study of problem solving in abstract machines. Here the abstract machines are called as mathematical model rather than it’s not a hardware. Automata theory has various types of automata such as Deterministic Finite Automata, Non-deterministic finite automata, Pushdown Automata and Linear Bounded Automata. These entire automata can be performed in a single hardware called “Turing Machine”. Till now nobody proved that, a problem that cannot be solved by a Turing Machine can be solved by a real world computer. The Computability speaks about “what are all the problems can be solved by a computer and cannot be solved by a computer”. This is called as decidability and un-decidability. The computational complexity theory speaks about “how much time and space an algorithm takes to solve a problem. This is called as Time and Space Complexity. These are the topics are discussed in this course “Principles in Theory of Computation”.

    Overview

    Section 1: Introduction

    Lecture 1 Introduction

    Section 2: Minimization of Finite Automata

    Lecture 2 Minimization of Finite Automata

    Section 3: Regular Expression to Finite Automata

    Lecture 3 Regular Expression to Finite Automata

    Section 4: Regular Expression to Finite Automata Continuation

    Lecture 4 Regular Expression to Finite Automata

    Section 5: Regular Expression to Finite Automata Continuation

    Lecture 5 Regular Expression to Finite Automata

    Section 6: Finite Automata to Regular Expression

    Lecture 6 Finite Automata to Regular Expression

    Section 7: Finite Automata to Regular Expression Continuation

    Lecture 7 Finite Automata to Regular Expression

    Section 8: Finite Automata to Regular Expression Continuation

    Lecture 8 Finite Automata to Regular Expression

    Section 9: Finite Automata to Regular Expression using Arden's Theorem

    Lecture 9 Finite Automata to Regular Expression using Arden's Theorem

    Section 10: Pumping Lemma for Regular Languages

    Lecture 10 Pumping Lemma for Regular Languages

    Section 11: Pumping Lemma for Regular Languages Continuation

    Lecture 11 Pumping Lemma for Regular Languages

    Section 12: Pumping Lemma for Regular Languages Continuation

    Lecture 12 Pumping Lemma for Regular Languages

    Section 13: Leftmost Derivation & Rightmost Derivation

    Lecture 13 Leftmost Derivation & Rightmost Derivation

    Section 14: Ambiguous Grammar

    Lecture 14 Ambiguous Grammar

    Section 15: Simplification of CFG

    Lecture 15 Simplification of CFG

    Section 16: Simplification of CFG continuation

    Lecture 16 Simplification of CFG

    Section 17: Simplification of CFG continuation

    Lecture 17 Simplification of CFG

    Section 18: Chomsky Normal Form (CNF)

    Lecture 18 Chomsky Normal Form (CNF)

    Section 19: Greibach Normal Form (GNF)

    Lecture 19 Greibach Normal Form (GNF)

    Section 20: Greibach Normal Form (GNF) Continuation

    Lecture 20 Greibach Normal Form (GNF)

    Section 21: Introduction to Pushdown Automata (PDA)

    Lecture 21 Introduction to Pushdown Automata (PDA)

    Section 22: Introduction to Pushdown Automata (PDA) Continuation

    Lecture 22 Introduction to Pushdown Automata (PDA)

    Section 23: Equivalence of PDA & CFG

    Lecture 23 Convert PDA to CFG

    Section 24: Equivalence of PDA & CFG Continuation

    Lecture 24 Convert PDA to CFG

    Section 25: Equivalence of PDA & CFG Continuation

    Lecture 25 CFG to PDA

    Section 26: Push Down Automata Solved Examples

    Lecture 26 Push Down Automata Solved Examples

    Section 27: Push Down Automata Solved Examples Continuation

    Lecture 27 Push Down Automata Solved Examples

    Section 28: Turing Machine Introduction

    Lecture 28 Turing Machine Introduction

    Section 29: Turing Machine Introduction Continuation

    Lecture 29 Turing Machine Introduction

    Section 30: Turing Machine Introduction Continuation

    Lecture 30 Turing Machine Introduction

    Section 31: Instantaneous Description of Turing Machine

    Lecture 31 Instantaneous Description of Turing Machine

    Section 32: Turing Machine Examples

    Lecture 32 Turing Machine Examples

    Section 33: Turing Machine Examples Continuation

    Lecture 33 Turing Machine Examples

    Section 34: Turing Machine Examples Continuation

    Lecture 34 Turing Machine Examples

    Section 35: Turing Machine Examples Continuation

    Lecture 35 Turing Machine Examples

    Section 36: Palindrome using Turing Machine

    Lecture 36 Palindrome using Turing Machine

    Section 37: Addition by Turing Machine

    Lecture 37 Addition by Turing Machine

    Section 38: Subtraction By Turing Machine

    Lecture 38 Subtraction By Turing Machine

    Section 39: 2's Complement by Turing Machine

    Lecture 39 2's Complement by Turing Machine

    Section 40: Multiplication by Turing Machine

    Lecture 40 Multiplication by Turing Machine

    Section 41: Multiplication by Turing Machine Continuation

    Lecture 41 Multiplication by Turing Machine

    Section 42: Multiplication by Turing Machine Continuation

    Lecture 42 Multiplication by Turing Machine

    Section 43: Division by Turing Machine

    Lecture 43 Division by Turing Machine

    Section 44: Division by Turing Machine Continuation

    Lecture 44 Division by Turing Machine

    Section 45: Division by Turing Machine Continuation

    Lecture 45 Division by Turing Machine

    Learner who is interested in theoretical computer Science,Learner who is interested in solving real world problems,Learner who is interested in developing a programming language