If I'm a student, I would've liked to learn more about other computational models (primarily, lambda calculus and recursive functions). More and more programming languages are incorporating functional features, and learning these subjects would give a great insight into this perspective of thinking about computation.
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LoginTeaching end-course material in a computational-models course
Course Queries Syllabus Queries 3 years ago
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manpreet![Tuteehub forum best answer]()
Best Answer
3 years ago
As TAs in an undergrad course on computational models, every year we are faced with a dilemma of what material to teach in the last few weeks of the course.
To be specific, our typical syllabus is as follows (basically the first few chapters of Sipser):
Finite Automata: DFA, NFA, determinization, Myhill-Nerode, etc.
Turing Machines: computability (RE,R, mapping-reductions, undecidability results).
Complexity classes: P, NP, PSPACE, karp-reductions, Savitch's theorem, Time/Space Hierarchy theorems.
Now comes the question of what to do next (usually we have about 3 weeks left).
Option 1: NL, coNL, logspace reductions and the Immerman Zzelepcsényi theorem.
Option 2: Randomized complexity (RP, BPP), but in a very low level, since probability is not a prerequisite for the course.
Option 3: Communication complexity - describing a communication model with some very initial results, describing IP and trying to prove