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This is an introductory course on the theory of computation. It covers topics such as computation, memory, CPUs, automata, and the power of Turing Machines. Explore the syllabus and learn about different computational models.
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CS-300 Theory of Computation Lecture 1
General Info for the Course Instructor: Dr.Mohammad El-Bashir Syllabus: tentative class schedule can be found in course web page Book: Introduction to the Theory of Computation Michael Sipser, 3rd edition Lecture 1
Outline of the course contents Computation memory CPU Lecture 1
temporary memory input CPU output Program memory Lecture 1
Example: temporary memory input CPU output Program memory compute compute Lecture 1
temporary memory input CPU output Program memory compute compute Lecture 1
temporary memory input CPU output Program memory compute compute Lecture 1
temporary memory input CPU Program memory output compute compute Lecture 1
Automaton temporary memory Automaton input CPU output Program memory Lecture 1
Automaton temporary memory Automaton input output transition state Lecture 1
Different Kinds of Automata • Automata are distinguished by the temporary memory • Finite Automata: no temporary memory • Pushdown Automata: stack • Turing Machines: random access memory Lecture 1
Finite Automaton temporary memory input Finite Automaton output Example: Elevators, Vending Machines (small computing power) Lecture 1
Pushdown Automaton Temp. memory Stack Push, Pop input Pushdown Automaton output Example: Compilers for Programming Languages (medium computing power) Lecture 1
Turing Machine Temp. memory Random Access Memory input Turing Machine output Examples: Any Algorithm (highest computing power) Lecture 1
Power of Automata Simple problems More complex problems Hardest problems Finite Automata Pushdown Automata Turing Machine Less power More power Solve more computational problems Lecture 1
Turing Machine is the most powerful computational model known Question: Are there computational problems that a Turing Machine cannot solve? Answer: Yes (unsolvable problems) Lecture 1
Time Complexity of Computational Problems: NP-complete problems Believed to take exponential time to be solved P problems Solved in polynomial time Lecture 1