algorithm: videos

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1. 1▶How To Build AI Voice Systems in 2026ConnerNotes
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2. 2▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 26): Leapfrog integration,adjoint methodJustin SolomonNotes
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3. 3▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 25): Exponential/RK/Newmark integrationJustin SolomonNotes
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4. 4▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 24): Ordinary differential equationsJustin SolomonNotes
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5. 5▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 23): Numerical integrals and derivativesJustin SolomonNotes
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6. 6▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 22): 1D Quadrature/Numerical IntegrationJustin SolomonNotes
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7. 7▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 21): InterpolationJustin SolomonNotes
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8. 8▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 20): Alternating optimization and ADMMJustin SolomonNotes
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9. 9▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 19): Gauss-Newton, Levenberg-MarquardtJustin SolomonNotes
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10. 10▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 18): Conjugate gradient algorithmJustin SolomonNotes
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11. 11▶6 5220 Lecture 4 Game theory, Lower Bounds 1, Coupon Collecting, Stable Matching.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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12. 12▶6 5220 Lecture 3 Adelman's theorem, Game tree evaluationMIT 6.5220 Randomized Algorithms Fall 2025Notes
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13. 13▶6 5220 Lecture 2 Min-cut, Complexity theory.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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14. 14▶6 5220 Lecture 1 Introduction to Randomized Algorithms. Quicksort, BSP.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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15. 15▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 17): Active set, barrier, intro to CGJustin SolomonNotes
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16. 16▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 16): Constrained optim., KKT conditions6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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17. 17▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 16): Constrained optim., KKT conditionsJustin SolomonNotes
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18. 18▶6 5220 Lecture 16: Parallel Maximal Independent Set. Derandomization.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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19. 19▶6 5220 Lecture 18 Sampling: transitive closure. DNF counting, rare events.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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20. 20▶6 5220 Lecture 14: Symmetry breaking. Parallel Algorithms. Ethernet. Perfect matching.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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21. 21▶6 5220 Lecture 13: Fingerprinting by polynomials, perfect matching, network coding.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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22. 22▶6 5220 Lecture 12: Text search. Bloom filters.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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23. 23▶6 5220 Lecture 11: Consistent Hashing. Fingerprinting.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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24. 24▶6 5220 Lecture 10: 2 Choices (cont). Cuckoo Hashing.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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25. 25▶6 5220 Lecture 8: The power of two choices.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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26. 26▶6 5220 Lecture 7: Chernoff Bound. Randomized routing.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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27. 27▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 15): BFGS and Quasi-Newton Methods6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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28. 28▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 15): BFGS and Quasi-Newton MethodsJustin SolomonNotes
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29. 29▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 14): Convergence of gradient descent6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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30. 30▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 14): Convergence of gradient descentJustin SolomonNotes
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31. 31▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 13): Golden sec search, Wolfe conditions6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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32. 32▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 13): Golden sec search, Wolfe conditionsJustin SolomonNotes
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33. 33▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 12): Broyden's method, root finding6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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34. 34▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 12): Broyden's method, root findingJustin SolomonNotes
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35. 35▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 11): Procrustes problem, root finding6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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36. 36▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 10): Re-deriving SVD, SVD applications6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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37. 37▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 9): QR iteration, intro to SVD6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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38. 38▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 7): Applications of eigenvalues6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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39. 39▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 6): QR factorization6.7350: Numerical Algorithms for Computing and Machine Learning (fall 2025)Notes
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40. 40▶6 5220 Lecture 6 Median finding. Pseudorandom numbers.MIT 6.5220 Randomized Algorithms Fall 2025Notes
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41. 41▶6.5220 Lecture 5 Deviations: Markov, Chebyshev. Balls in BinsMIT 6.5220 Randomized Algorithms Fall 2025Notes
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42. 42▶How to Make a VibeCode Server (and why you should)ConnerNotes
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43. 43▶Code Rails 10x Faster: AI-Powered Dev with CursorConnerNotes
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44. 44▶Lesson 23: Network Algorithms and Approximations by Mohammad Hajiaghayi: Iterative Rounding Method 2Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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45. 45▶Lesson 22: Network Algorithms and Approximations by Mohammad Hajiaghayi: Iterative Rounding Method 1Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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46. 46▶Lesson 21: Network Algorithms and Approximations by Mohammad Hajiaghayi:Con Facility & Group SteinerNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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47. 47▶Lesson 20: Network Algorithms and Approximations by Mohammad Hajiaghayi: Metric Facility LocationNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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48. 48▶Lecture 23 - Exam reviewCSE 6220 / CX 4220 Spring 2025Notes
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49. 49▶Lesson 19: Network Algorithms and Approximations by Mohammad Hajiaghayi: k-Center and k-MedianNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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50. 50▶Lecture 22 - Graph OptimizationCSE 6220 / CX 4220 Spring 2025Notes
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