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randomized algorithms

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  1. 6 5220 Lecture 4 Game theory, Lower Bounds 1, Coupon Collecting, Stable Matching.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  2. 6 5220 Lecture 3 Adelman's theorem, Game tree evaluationMIT 6.5220 Randomized Algorithms Fall 2025Notes
  3. 6 5220 Lecture 2 Min-cut, Complexity theory.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  4. 6 5220 Lecture 1 Introduction to Randomized Algorithms. Quicksort, BSP.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  5. 6 5220 Lecture 16: Parallel Maximal Independent Set. Derandomization.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  6. 6 5220 Lecture 18 Sampling: transitive closure. DNF counting, rare events.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  7. 6 5220 Lecture 14: Symmetry breaking. Parallel Algorithms. Ethernet. Perfect matching.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  8. 6 5220 Lecture 13: Fingerprinting by polynomials, perfect matching, network coding.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  9. 6 5220 Lecture 12: Text search. Bloom filters.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  10. 6 5220 Lecture 11: Consistent Hashing. Fingerprinting.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  11. 6 5220 Lecture 10: 2 Choices (cont). Cuckoo Hashing.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  12. 6 5220 Lecture 8: The power of two choices.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  13. 6 5220 Lecture 7: Chernoff Bound. Randomized routing.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  14. 6 5220 Lecture 6 Median finding. Pseudorandom numbers.MIT 6.5220 Randomized Algorithms Fall 2025Notes
  15. 6.5220 Lecture 5 Deviations: Markov, Chebyshev. Balls in BinsMIT 6.5220 Randomized Algorithms Fall 2025Notes
  16. Lovász Local lemma (Randomized algorithms, Fall 2022, Lecture 15)Randomized Algorithms, Fall 2022Notes
  17. PAC learning (Randomized algorithms, Fall 2022, Lecture 13)Randomized Algorithms, Fall 2022Notes
  18. Entropy and codes (Randomized algorithms, Fall 2022, Lecture 14)Randomized Algorithms, Fall 2022Notes
  19. Geometric sampling (Randomized algorithms, Fall 2022, Lecture 12)Randomized Algorithms, Fall 2022Notes
  20. Randomized tree metrics (Randomized algorithms, Fall 2022, Lecture 11)Randomized Algorithms, Fall 2022Notes
  21. Sparsest cut (Randomized algorithms, Fall 2022, Lecture 10)Randomized Algorithms, Fall 2022Notes
  22. Locality-sensitive hashing (Randomized algorithms, Fall 2022, Lecture 9)Randomized Algorithms, Fall 2022Notes
  23. Dimensionality reduction (Randomized algorithms, Fall 2022, Lecture 8)Randomized Algorithms, Fall 2022Notes
  24. Distinct elements (Randomized algorithms, Fall 2022, Lecture 7)Randomized Algorithms, Fall 2022Notes
  25. Randomized rounding (Randomized algorithms, Fall 2022, Lecture 6)Randomized Algorithms, Fall 2022Notes
  26. Random sums and graphs (Randomized algorithms, Fall 2022, Lecture 5)Randomized Algorithms, Fall 2022Notes
  27. Randomized minimum cut (Randomized algorithms, Fall 2022, Lecture 4)Randomized Algorithms, Fall 2022Notes
  28. Hash tables and linear probing (Randomized algorithms, Fall 2022, Lecture 3)Randomized Algorithms, Fall 2022Notes
  29. Hashing and heavy hitters (Randomized algorithms, Fall 2022, Lecture 2)Randomized Algorithms, Fall 2022Notes
  30. Randomized searching and sorting (Randomized algorithms, Fall 2022, Lecture 1)Randomized Algorithms, Fall 2022Notes
  31. CSE290A, Spring 2020: Lec 16, the Count-Min sketch, and the AMS algorithm for frequency momentsCSE290A, Spring 2020: Randomized AlgorithmsNotes
  32. CSE290A, Spring 2020: Lec 15, improved distinct elements and introduction to heavy hittersCSE290A, Spring 2020: Randomized AlgorithmsNotes
  33. CSE290A, Spring 2020: Lec 14, counting distinct elements in the streaming modelCSE290A, Spring 2020: Randomized AlgorithmsNotes
  34. CSE290A, Spring 2020: Lec 12, Introduction to distribution testingCSE290A, Spring 2020: Randomized AlgorithmsNotes
  35. CSE290A, Spring 2020: Lec 11, the Poisson approximating and PoissonlzationCSE290A, Spring 2020: Randomized AlgorithmsNotes
  36. CSE290A, Spring 2020: Lec 9, proof of the Chernoff upper tail, discussion of the tail boundCSE290A, Spring 2020: Randomized AlgorithmsNotes
  37. CSE290A, Spring 2020: The Johnson-Lindenstrauss lemmaCSE290A, Spring 2020: Randomized AlgorithmsNotes
  38. CSE290A, Spring 2020: Estimating the average degree of a graphCSE290A, Spring 2020: Randomized AlgorithmsNotes
  39. CSE290A, Spring 2020: Lec 7, Walker's alias methodCSE290A, Spring 2020: Randomized AlgorithmsNotes
  40. CSE290A, Randomized Algorithms: Lec6, Cohen-Lewis matrix multiplicationCSE290A, Spring 2020: Randomized AlgorithmsNotes
  41. CSE290A, Spring 2020: Lec 5, improved Karp-Luby-MadrasCSE290A, Spring 2020: Randomized AlgorithmsNotes
  42. CSE290A Spring 2020: Lec 4, Karp-Luby importance samplingCSE290A, Spring 2020: Randomized AlgorithmsNotes
  43. CSE290A, Spring 2020: Lec 3, Chernoff boundsCSE290A, Spring 2020: Randomized AlgorithmsNotes
  44. CSE290A, Spring 2020: Lec 2, the Hoeffing boundCSE290A, Spring 2020: Randomized AlgorithmsNotes
  45. CSE290A, Spring 2020, Lec1: Quicksort and Karger's mincutCSE290A, Spring 2020: Randomized AlgorithmsNotes