Theory of Computation: What Computers Can and Can't Do
Theory of Computation: leave with a finite automaton for a validation rule, its regex and an NP-hard problem approximation note.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why theory of computation matters — the common problem: Regular expressions are copied blindly and NP-hard problems are attacked with brute force.
- 2Core concepts in plain language: Finite automata, formal languages, grammars, regular expressions, pushdown automata, Turing machines
- 3Going further: computability, decidability, reductions, complexity classes, NP-completeness, approximation algorithms
- 4Framework walkthrough: Big-O Complexity Analysis, Computational Thinking, Worked Problem Sets
- 5Practical workflow, built live: A finite automaton for a validation rule, its regex and an NP-hard problem approximation note.
- 6How to measure it: Correctness on test cases, Time and space complexity, Problems solved independently
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A finite automaton for a validation rule, its regex and an NP-hard problem approximation note.
- A working understanding of Big-O Complexity Analysis and Computational Thinking
- A short list of measures to track: Correctness on test cases, Time and space complexity, Problems solved independently
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