Date Class Period & Lecture Topic Reading Assignment
Sep 03 1. What is the class about and how will it work?
(Syllabus, policies, business)
     
Sep 05 2. What tools should we use?
(Introduction and basic concepts)
Chapter 0 pp. 1-25  
Sep 08 3. Can we build a simple model of computation?
(Finite automata)
Chapter 1 pp. 31-43 1
Sep 10 4. What if we include a bit of magic in our model?
(Nondeterminism)
pp. 47-54 2
Sep 12 5. What? Magic doesn't help?
(Equivalence of DFAs and NFAs)
  pp. 54-63 3
Sep 15 6. Can we think of this another way?
(Regular expressions)
  pp. 63-69 4
Sep 17 7. And, is this really the same thing?
(Kleene's theorem)
  pp. 69-76 5
Sep 19 8. So, is this model good enough?
(Nonregular languages)
  pp. 77-83 6
Sep 22 9. What if we aren't sure about how our "world" works?
(Markov Chains)
  Additional Reading 1 7
Sep 24 10. What if we know even less about our "world"?
(Hidden Markov Models)
  Additional Reading 2 8
Sep 26 11. Ok, then how about this better model?
(Context free grammars)
Chapter 2 pp. 101-107 9
Sep 29 12. This is getting confusing; can we do avoid that?
(Ambiguity and normal form)
  pp. 107-111 10
Oct 01 13. How is this new model like our old one?
(Pushdown automata)
  pp. 111-116 11
Oct 03 14. And are we thinking of it the right way?
(Equivalence of CFGs and PDAs)
  pp. 117-124 12
Oct 06 15. So, now this better model is good enough, right?
(Non-context free languages)
  pp. 125-129 13
Oct 08 16. What do we know so far?
(Review)
    14
  Midterm 1 (October 7–10 in the testing center)
Oct 10 17. No lecture (Midterm)      
Oct 13 18. Why Should I consider Grad School?      
Oct 15 19. How can we improve our model yet again?
(Turing machines)
Chapter 3 pp. 165-173  
Oct 17 20. And, what about some other possible improvements?
(Variations on Turing machines)
  pp. 174-182 15
Oct 20 21. And, what does any of this have to do with writing code, anyway?
(Algorithms)
  pp. 182-187 16
Oct 22 22. Ok, so what can our model do?
(Decidable languages)
Chapter 4 pp. 193-200 17
Oct 24 23. Wait, there's something it can't do?
(Halting problem)
  pp. 201-207 18
Oct 27 24. Wait, there's lots of stuff it can't do?
(Undecidable and unrecognizable)
  pp. 207-210 19
Oct 29 25. How can I tell what it can't do?
(Reducibility)
Chapter 5 pp. 215-220 20
Oct 31 26. Reducibility cont.   pp. 220-226 21
Nov 03 27. Even more reducibility   22
Nov 05 28. Can we, again, look at this another way?
(Post correspondence problem)
  pp. 227-233
Nov 07 29. And this helps us how?
(Mapping [many-one] reducibility)
  pp. 234-238 23
Nov 10 30. So what do we know now?
(Review)
    24
  Midterm 2 (November 10–13 in the testing center)
Nov 12 31. No lecture (Midterm)      
Nov 14 32. How can we decide if an algorithm is practical?
(Big O analysis)
Chapter 7 pp. 275-284  
Nov 17 33. What can our model do practically?
(The class P)
  pp. 284-291 25
Nov 19 34. Wait, not everything it can do is practical?
(The class NP)
  pp. 292-298 26
Nov 21 35. Are you sure?
(The class NP-complete)
  pp. 299-304 27
Nov 24 36. So, magic might be useful after all?
(Cook's theorem)
  pp. 304-311 28
Nov 25 37. Are there are lot of these nasty problems?
(More NP-complete problems)
  pp. 311-322 29
Dec 01 38. Probabilistic Turing Machines   Additional Reading 3 30
Dec 03 39. No lecture (ICMLA)
Dec 05 40. Naive Bayes   Additional Reading 4 31
Dec 08 41. What about other ways of computing?
(Quantum Computing, etc.???)
    32
Dec 10 42. Finally, what do we know?
(Review)
     
Dec 12 Reading Day      
Dec 17 Sec. 1 Final (11:00am–2:00pm in class)
Dec 19 Sec. 2 Final (7:00am–10:00am in class)

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