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Theory Of Computation By Dr. K Venkatesh
Published 2/2026
Created by Dr. K Venkatesh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: en-IN | Duration: 7 Lectures ( 2h 58m ) | Size: 4.8 GB[/center]
Automata, Regular expression, Push down automata, Turing machine
What you'll learn
✓ Understand and model computation using formal languages, grammars, and automata.
✓ Analyze the computational power and limitations of finite automata, pushdown automata, and Turing machines.
✓ Classify problems by decidability and complexity, identifying decidable, undecidable, and intractable problems.
✓ Apply formal methods to design and verify language recognizers and computational models.
Requirements
● Basic mathematics
Description
Theory of Computation (ToC) is a core and foundational course in computer science that focuses on understanding how computational systems work, what problems computers can solve, and the inherent limitations of computation. This course provides a deep theoretical framework that underpins modern computing, programming languages, algorithms, and artificial intelligence.
The course begins with finite automata and regular languages, helping learners model simple computational problems and recognize patterns using formal machines. It then progresses to context-free grammars and pushdown automata, which form the basis of syntax analysis and compiler design. A major component of the course is the study of Turing machines, the most powerful abstract model of computation, which helps learners understand the concept of algorithmic solvability. Topics such as decidability and undecidability clearly explain why certain problems cannot be solved by any computer program. The course also introduces computational complexity, offering insights into problem efficiency and classes such as P and NP.
Designed with clear explanations, intuitive reasoning, and step-by-step examples, this course avoids heavy mathematics and focuses on conceptual clarity. It is especially useful for students preparing for competitive exams, pursuing advanced research, or aiming to strengthen their understanding of core computer science subjects. By the end of the course, learners will develop strong analytical skills and a solid theoretical foundation essential for higher-level computing studies.
Who this course is for
■ This course is intended for undergraduate students in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, and related disciplines who seek a strong theoretical foundation in computation, formal languages, and algorithmic problem solving
Theory Of Computation By Dr. K Venkatesh
Published 2/2026
Created by Dr. K Venkatesh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: en-IN | Duration: 7 Lectures ( 2h 58m ) | Size: 4.8 GB[/center]
Automata, Regular expression, Push down automata, Turing machine
What you'll learn
✓ Understand and model computation using formal languages, grammars, and automata.
✓ Analyze the computational power and limitations of finite automata, pushdown automata, and Turing machines.
✓ Classify problems by decidability and complexity, identifying decidable, undecidable, and intractable problems.
✓ Apply formal methods to design and verify language recognizers and computational models.
Requirements
● Basic mathematics
Description
Theory of Computation (ToC) is a core and foundational course in computer science that focuses on understanding how computational systems work, what problems computers can solve, and the inherent limitations of computation. This course provides a deep theoretical framework that underpins modern computing, programming languages, algorithms, and artificial intelligence.
The course begins with finite automata and regular languages, helping learners model simple computational problems and recognize patterns using formal machines. It then progresses to context-free grammars and pushdown automata, which form the basis of syntax analysis and compiler design. A major component of the course is the study of Turing machines, the most powerful abstract model of computation, which helps learners understand the concept of algorithmic solvability. Topics such as decidability and undecidability clearly explain why certain problems cannot be solved by any computer program. The course also introduces computational complexity, offering insights into problem efficiency and classes such as P and NP.
Designed with clear explanations, intuitive reasoning, and step-by-step examples, this course avoids heavy mathematics and focuses on conceptual clarity. It is especially useful for students preparing for competitive exams, pursuing advanced research, or aiming to strengthen their understanding of core computer science subjects. By the end of the course, learners will develop strong analytical skills and a solid theoretical foundation essential for higher-level computing studies.
Who this course is for
■ This course is intended for undergraduate students in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, and related disciplines who seek a strong theoretical foundation in computation, formal languages, and algorithmic problem solving
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