Introduction to Automata Theory, Languages and Computation

Introduction to Automata Theory, Languages and Computation, available at $79.99, has an average rating of 4.5, with 81 lectures, 6 quizzes, based on 178 reviews, and has 1376 subscribers.

You will learn about Understand the basics of Automata Theory, Languages and its need Understand the working of Finite Automata, Push Down Automata and Turing Machine Able to solve problems using Finite Automata, Push Down Automata and Turing Machine Able to recognize the relationship between various Automata Understand the need for proving various equivalence between Automata This course is ideal for individuals who are Computer science students or Students preparing for Gate exams or Anyone planing for Government Exams in Computer Science base or Students interested in understanding the basic working of Models It is particularly useful for Computer science students or Students preparing for Gate exams or Anyone planing for Government Exams in Computer Science base or Students interested in understanding the basic working of Models.

Enroll now: Introduction to Automata Theory, Languages and Computation

Summary

Title: Introduction to Automata Theory, Languages and Computation

Price: $79.99

Average Rating: 4.5

Number of Lectures: 81

Number of Quizzes: 6

Number of Published Lectures: 74

Number of Published Quizzes: 6

Number of Curriculum Items: 87

Number of Published Curriculum Objects: 80

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basics of Automata Theory, Languages and its need

Understand the working of Finite Automata, Push Down Automata and Turing Machine

Able to solve problems using Finite Automata, Push Down Automata and Turing Machine

Able to recognize the relationship between various Automata

Understand the need for proving various equivalence between Automata

Who Should Attend

Computer science students

Students preparing for Gate exams

Anyone planing for Government Exams in Computer Science base

Students interested in understanding the basic working of Models

Target Audiences

Computer science students

Students preparing for Gate exams

Anyone planing for Government Exams in Computer Science base

Students interested in understanding the basic working of Models

The aim of this course “Introduction to Automata Theory, Languages and Computation” is to give a detailed working explanation regarding each Mathematical model, its corresponding languages, and their provable equivalence. “Theory of Computation” has three major subdivisions namely

1) Automata Theory

2) Computability Theory

3) Complexity Theory

The automata theory deals with some Mathematical models that perform some operations automatically like programming machines. There are four main Mathematical models namely, Finite Automata(FA), Push Down Automata(PDA), Linear Bound Automata(LBA), and Turing Machine(TM). Each Mathematical model differs based on its memory units as FA has no external memory unit, PDA has stack as a memory unit, LBA has finite length tape as a memory unit and TM has infinite tape as a memory unit.

Based on the limitations in the memory unit each model solves a limited set of problems only. The set of problems solved by each model is grouped as languages accepted by the model. The problems solved by Finite Automata are called Regular Language and its corresponding language representation is called Regular Grammar. The language accepted by Push Down Automata is called Context Free Language, the language accepted by Linear Bound Automata is called Context Sensitive Language, and the language accepted by Turing Machine is called Un-Restricted language since Turing machines have unlimited memory and random access to the memory unit.

Turing machines can be equated to modern computers, it can solve any problem that is solvable by computers. Computability theory deals with verifying whether the problem is solvable or not and If it is solvable complexity theory deals with the algorithmic complexity of problems that are solvable by Turing Machine.

This course mainly deals with automata theory (Mathematical Models) and its languages.

Course Curriculum

Chapter 1: Introduction to Automata Theory, Languages and Computation

Lecture 1: Introduction to this course

Lecture 2: Introduction to Automata – Languages – Chomsky Hierarchy

Lecture 3: Formal Languages: Strings, Languages

Lecture 4: Chomsky Hierarchy

Chapter 2: Introduction to Finite Automata

Lecture 1: Introduction to Finite Automata

Lecture 2: DFA and NFA

Lecture 3: DFA – Extended Transition Function and Language Acceptance

Lecture 4: NFA – Extended Transition Function and Language Acceptance

Lecture 5: Examples of DFA and NFA – language of strings

Lecture 6: Example DFA for complement of any language

Lecture 7: Finite Automaton with - moves

Lecture 8: Finite Representation : Regular Expressions (RE)

Lecture 9: Pumping Lemma for RE

Chapter 3: Equivalence among Finite Automata

Lecture 1: Equivalence of NFA, DFA, NFA with - moves and RE

Lecture 2: Working and examples for Subset construction Method

Lecture 3: NFA to DFA using Lazy subset construction Method

Lecture 4: Thomsons Construction Method for RE to ?-NFA

Lecture 5: Regular Expression to DFA

Lecture 6: Solved Examples for RE to DFA

Lecture 7: Minimization of DFA

Lecture 8: Epsilon NFA to NFA

Lecture 9: DFA to RE (Formula Method)

Lecture 10: Solved Examples for DFA to RE (Formula Method)

Lecture 11: Solved Examples for DFA to RE 3 states (Formula method)

Lecture 12: DFA to RE (State Elimination Method)

Lecture 13: Finite Automata – Example 1 – Scenario Question

Lecture 14: Finite Automata – Example 2 – Scenario based question

Lecture 15: Finite Automata – Example 3 – Scenario based question for DFA construction

Chapter 4: Automata With Output

Lecture 1: Introduction to Moore and Mealy Machine

Lecture 2: Moore machine Example and Three ways of representation

Lecture 3: Mealy machine Example and Three ways of representation

Lecture 4: Moore Machine to Mealy Machine

Lecture 5: Mealy machine to Moore machine

Chapter 5: Context Free Garmmar

Lecture 1: Introduction, Types of Grammar and Representation

Lecture 2: Context Free Grammar (CFG) and Languages

Lecture 3: Derivation, Parse tree and Ambiguity

Lecture 4: Simplification of CFG – Elimination of Useless Symbols

Lecture 5: Simplification of CFG – Elimination of Null productions

Lecture 6: Simplification of CFG – Elimination of Unit productions

Lecture 7: CFG to Chomsky Normal Form (CNF)

Lecture 8: Solved Examples – CFG to CNF

Lecture 9: CFG to Greibach Normal form

Lecture 10: Solved Examples – CFG to GNF

Lecture 11: Solved Examples – CNF to GNF

Lecture 12: Example 1 – Scenario based question for CNF and GNF

Chapter 6: Push Down Automata

Lecture 1: Introduction to Push Down Automata

Lecture 2: Problem in PDA {a^nb^n/n>1}

Lecture 3: Problem in PDA {a^n b^ m/n,m>=1} two problems (n>m) and (m>n)

Lecture 4: Problem in PDA { WCW^R/ w={a,b}*}

Lecture 5: Non-Deterministic PDA {Palindrome of a string}

Lecture 6: CFG (Context Free Grammar) to PDA (Push Down Automata)

Lecture 7: PDA (Push Down Automata) to CFG (Context Free Grammar)

Lecture 8: Pumping Lemma for CFG

Lecture 9: Scenario based question in CFG and PDA

Chapter 7: Turing Machine

Lecture 1: Introduction to Turing Machine, Instantaneous Description

Lecture 2: Turing Machine Representations

Lecture 3: Turing Machine for Regular Language

Lecture 4: Turing Machine for Context Free Language

Lecture 5: TM for L={a^n b^n c^n / n>=0}

Lecture 6: TM for palindrome of a string over alphabet a,b

Lecture 7: TM for proper subtraction

Lecture 8: TM for Multiplication

Lecture 9: Modifications of TM

Lecture 10: Simulation of Turing Machine

Chapter 8: Extra solved problems in Finite Automata, Push Down Automata and Turing Machine

Lecture 1: Example 1 : Regular Language – construction of DFA, PDA and TM

Lecture 2: Example 2 : Context Free Language – Construction of PDA and TM

Lecture 3: Example 3 : Turing Machine problem

Lecture 4: Example 4 : Turing Machine for 1s Complement

Lecture 5: Example 5 : scenario based question in Turing Machine

Lecture 6: Example 6 : scenario based question in Turing Machine

Lecture 7: Example 7 : scenario based question in Turing Machine

Lecture 8: Example 8 : scenario based question in PDA and CFG

Chapter 9: Decidability and Undecidability

Lecture 1: Decidability and Undecidability

Chapter 10: Class P and Class NP problems

Lecture 1: Scenario based question in PCP

Instructors

Dr.Deeba K
Assistant Professor in SRM IST, Kattankulathur, Chennai

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 11 votes

4 stars: 58 votes

5 stars: 105 votes

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