uCertify

Theory of Computation: Automata, Formal Languages, Computation and Complexity

Master Theory of Computation: Automata, Formal Languages, and Complexity. Understand foundational limits and power of computation for robust system design.

(THEORY-CMPUTATION.AU1) / ISBN : 979-8-90059-032-5
Lessons
Lab
AI Tutor (Add-on)
Get A Free Trial

About This Course

This course dives deep into the foundational 'Theory of Computation,' equipping you to understand the absolute limits and capabilities of algorithms. We'll dissect 'Automata Theory,' from finite automata to pushdown automata, modeling computation and language recognition. You'll then confront the power and constraints of 'Turing Machines,' exploring computability, decidability, and the profound implications of unsolvable problems. Finally, we tackle 'Computational Complexity Theory,' including the critical 'P vs. NP' problem, to analyze algorithmic efficiency and identify truly intractable challenges. This isn't just academic; it's about building systems that don't fail due to fundamental computational constraints, a critical skill for any serious engineer.

Skills You’ll Get

  • Design and analyze finite automata and pushdown automata to model computational processes, understanding their inherent limitations in language recognition and parsing.
  • Deconstruct and construct regular and context-free languages using grammars and expressions, identifying their closure properties and practical parsing challenges in compiler design.
  • Implement and extend Turing machine models to explore the boundaries of computability, distinguishing between decidable and undecidable problems and their real-world implications for algorithm design.
  • Evaluate the time and space complexity of algorithms, grasping the critical distinctions between P, NP, and NP-Complete problems to manage intractable computational tasks and resource allocation.

Download Course Outline

1

Introduction

  • Significance of Theory
  • Background and Motivation
  • The Scope, Organization, and Approach
2

Mathematical Preliminaries

  • Introduction
  • Set Theory
  • Theorems and Proofs
  • Russell’s Paradox and Cantor’s Diagonal Argument
  • Set Relations
  • Functions
  • Graph Theory
  • Algebraic Theory of Machines
  • Operations on Strings
  • Strings and Languages
  • Closure Properties of Languages
  • Representation of Languages
  • Summary
3

Finite Automata and Regular Expressions

  • Introduction
  • Automata Theory
  • Modeling of Programs and Computation
  • Finite Automata Model
  • Regular Expressions
  • Applications of Finite Automata
  • Automata Concepts Through Games
  • Summary
4

Variants of Finite Automata

  • Introduction
  • Nondeterministic Finite Automata Model
  • Properties of NFA
  • Equivalence of NFA and DFA
  • Two-way Finite Automata
  • Finite Automata with Output
  • Equivalence of Moore and Mealy Machines
  • Finite-State Transducers
  • Summary
5

Minimization of Finite Automata

  • Introduction
  • From Regular Expression to DFA
  • Formalism for Minimization
  • Minimization of Finite Automata
  • NFA Homomorphism
  • State Minimization Based on Equivalence Classes
  • Myhill–Nerode Theorem
  • Partitioning State Space
  • Partition-Refine Algorithm
  • Table-filling Algorithm
  • Equivalences of Regular Expressions
  • Summary
6

Regular Languages

  • Introduction
  • Regular Languages
  • Closure Properties of Regular Languages
  • On Regularity of Languages
  • DFA to Regular Expression
  • Pumping Lemma for Regular Languages
  • On Nonregularity of Languages
  • Myhill–Nerode Theorem and Its Applications
  • Regular Expression to DFA Using MN
  • Summary
7

Context-Free Grammars and Languages

  • Introduction
  • Context-Free Grammars
  • Relation Between FA and Regular Grammars
  • Derivation of Sentences
  • Closure Properties of Context-Free Languages
  • Parsing Context-Free Languages
  • Parsing Arithmetic Expressions
  • Ambiguous Grammars and Languages
  • Disambiguating Ambiguous Grammars
  • Operator-Precedence Grammars
  • Classifying Context-Free Grammars
  • Invertible Grammars
  • Summary
8

Normal Forms of Context-Free Grammars

  • Introduction
  • Simplification of Grammars
  • Chomsky Normal Form
  • Greibach Normal Form
  • Converting CNF to GNF Grammar
  • Complexity Analysis of GNF Grammar
  • Pumping Lemma for Context-Free Languages
  • Decidable Properties of Context-Free Languages
  • Summary
9

Pushdown Automata and Parsers

  • Introduction
  • The Idea of Parsing
  • Pushdown Automata Model
  • Formalism of PDA
  • Pushdown Automata Simulation
  • Types of PDAs
  • Language Acceptability by PDA
  • Equivalence of PDA and Context-Free Grammar
  • Parsers
  • LL(k) and LR(k) Grammars
  • LL Parser
  • LR Parser
  • Parallelization
  • Summary
10

Turing Machine and Computability

  • Introduction
  • Algorithmic Complexity
  • Analysis of Human Computation
  • Turing Machine Model
  • Language Recognition by Turing Machine
  • Turing Machines and Formal Languages
  • Computability
  • Church-Turing Thesis
  • Post Machine
  • Turing Machine Simulation
  • Computing Beyond the Turing Limit
  • Summary
11

Extensions of Basic Turing Machine

  • Introduction
  • Multi-tape Turing Machine
  • Linear Speedup
  • Multiple-track Turing Machine
  • Nondeterministic Turing Machine
  • Turing Machine and Type-0 Grammars
  • Universal Turing Machine
  • The Halting Problem of TM
  • Modern Computer Simulation on Turing Machine
  • Computing Frameworks
  • Summary
12

Linear-Bounded Automata and Context-Sensitive Languages

  • Introduction
  • Linear-Bounded Automata Model
  • Language Acceptability by LBA
  • Context-Sensitive Grammars and Languages
  • Non-context-free Properties of Programming Languages
  • Chomsky-Hierarchy Grammars and Languages
  • Closure Properties of Context-Sensitive Languages
  • Indexed Grammars
  • Regulated Rewriting Grammars
  • Conjunctive Grammars
  • Universal Versus Chomskian Grammars
  • Summary
13

Decidability, Undecidability, and Unsolvability

  • Introduction
  • Recursive and Recursive Enumerable Sets
  • Computable Sets
  • Recursive and Recursively Enumerable Languages
  • Decision Problems
  • Some Standard Problems and Their Intuitive Algorithms
  • Decidable Properties of Grammars and Languages
  • Enumeration of Languages and Turing Machines
  • Gödel Numbering
  • Unsolvability
  • Undecidability
  • Undecidability of Halting Problem
  • Rice’s Theorem
  • Summary
14

Computational Complexity Theory

  • Introduction
  • Determining Time Complexity
  • Time Complexity of Arithmetic Operations
  • Multi-tape Turing Machine Complexity
  • Concepts of Time and Space Complexities
  • Computational Complexity
  • Time- and Space-Bounded Complexities
  • Time-Bounded Complexity Classes
  • Space-Bounded Complexity Classes
  • Canonical Complexity Classes
  • Circuit Complexity
  • Reducibility
  • Primality and Compositness
  • Summary
15

NP-Completeness

  • Introduction
  • Class NP
  • Boolean Satisfiability
  • Cook–Levin Theorem
  • NP-Completeness
  • NP-Complete Problems
  • Solving SAT Problems
  • Counting Problems
  • Summary

Any questions?
Check out the FAQs

  Want to Learn More?

Contact Us Now

This course focuses on the fundamental capabilities and limitations of computation. You'll explore automata theory, formal languages, computability via Turing machines, and the complexity classes like P and NP, understanding what can and cannot be efficiently computed.

Automata Theory provides models for computation, essential for designing compilers, parsers, and state machines. It helps engineers understand the inherent limitations of certain problem types, preventing the pursuit of impossible or inefficient solutions.

We thoroughly cover Turing Machines as the ultimate model of computation, exploring their design, extensions, and their role in defining computability. This understanding is critical for grasping the theoretical limits of what any algorithm can achieve.

The primary focus is on Theory of Computation and mathematical modeling. However, you will engage in hands-on labs where you construct state diagrams and simulate machine behavior, ensuring you can visualize how these abstract concepts translate into computational reality.

Ready to Build Certified Computing Solutions?

  Join our Theory of Computation program today to master the logical foundations that drive the future of technology.

$97.99

Buy Now

Related Courses

All Courses
We use cookies for improving site experience and analytics. Learn More