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1. 1▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 26): Leapfrog integration,adjoint methodJustin SolomonNotes
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2. 2▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 25): Exponential/RK/Newmark integrationJustin SolomonNotes
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3. 3▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 24): Ordinary differential equationsJustin SolomonNotes
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4. 4▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 23): Numerical integrals and derivativesJustin SolomonNotes
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5. 5▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 22): 1D Quadrature/Numerical IntegrationJustin SolomonNotes
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6. 6▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 21): InterpolationJustin SolomonNotes
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7. 7▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 20): Alternating optimization and ADMMJustin SolomonNotes
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8. 8▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 19): Gauss-Newton, Levenberg-MarquardtJustin SolomonNotes
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9. 9▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 18): Conjugate gradient algorithmJustin SolomonNotes
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10. 10▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 17): Active set, barrier, intro to CGJustin SolomonNotes
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11. 11▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 16): Constrained optim., KKT conditionsJustin SolomonNotes
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12. 12▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 15): BFGS and Quasi-Newton MethodsJustin SolomonNotes
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13. 13▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 14): Convergence of gradient descentJustin SolomonNotes
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14. 14▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 13): Golden sec search, Wolfe conditionsJustin SolomonNotes
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15. 15▶Numerical Algorithms for Computing & ML, fall 2025 (lecture 12): Broyden's method, root findingJustin SolomonNotes
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16. 16▶MLSP Fall 2024 | Lecture11 - Independent Component AnalysisFall 2024Notes
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17. 17▶MLSP Fall 2024 | Lecture12 - ClusteringFall 2024Notes
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18. 18▶MLSP Fall 2024 | Lecture 9 - NMFFall 2024Notes
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19. 19▶MLSP Fall 2024 | Lecture 5 - Deterministic RepresentationsFall 2024Notes
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20. 20▶MLSP Fall 2024 | Lecture 6 - Data Driven RepresentationsFall 2024Notes
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21. 21▶MLSP Fall 2024 | Lecture 7 - Classification and MetaclassifierFall 2024Notes
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22. 22▶MLSP Fall 2024 | Lecture 8 - Face DetectionFall 2024Notes
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23. 23▶MLSP Fall 2024 | Lecture 4 - OptimizationFall 2024Notes
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24. 24▶MLSP Fall 2024 | Lecture 2 - Linear Algebra-IFall 2024Notes
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25. 25▶MLSP Fall 2024 | Lecture 1 - IntroductionFall 2024Notes
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26. 26▶MLSP Fall 2023 | Lecture 3 - Linear Algebra 2Fall 2024Notes
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27. 27▶Lovász Local lemma (Randomized algorithms, Fall 2022, Lecture 15)Randomized Algorithms, Fall 2022Notes
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28. 28▶PAC learning (Randomized algorithms, Fall 2022, Lecture 13)Randomized Algorithms, Fall 2022Notes
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29. 29▶Entropy and codes (Randomized algorithms, Fall 2022, Lecture 14)Randomized Algorithms, Fall 2022Notes
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30. 30▶Geometric sampling (Randomized algorithms, Fall 2022, Lecture 12)Randomized Algorithms, Fall 2022Notes
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31. 31▶Randomized tree metrics (Randomized algorithms, Fall 2022, Lecture 11)Randomized Algorithms, Fall 2022Notes
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32. 32▶Sparsest cut (Randomized algorithms, Fall 2022, Lecture 10)Randomized Algorithms, Fall 2022Notes
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33. 33▶Locality-sensitive hashing (Randomized algorithms, Fall 2022, Lecture 9)Randomized Algorithms, Fall 2022Notes
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34. 34▶Dimensionality reduction (Randomized algorithms, Fall 2022, Lecture 8)Randomized Algorithms, Fall 2022Notes
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35. 35▶Distinct elements (Randomized algorithms, Fall 2022, Lecture 7)Randomized Algorithms, Fall 2022Notes
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36. 36▶Randomized rounding (Randomized algorithms, Fall 2022, Lecture 6)Randomized Algorithms, Fall 2022Notes
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37. 37▶Random sums and graphs (Randomized algorithms, Fall 2022, Lecture 5)Randomized Algorithms, Fall 2022Notes
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38. 38▶Randomized minimum cut (Randomized algorithms, Fall 2022, Lecture 4)Randomized Algorithms, Fall 2022Notes
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39. 39▶Hash tables and linear probing (Randomized algorithms, Fall 2022, Lecture 3)Randomized Algorithms, Fall 2022Notes
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40. 40▶Hashing and heavy hitters (Randomized algorithms, Fall 2022, Lecture 2)Randomized Algorithms, Fall 2022Notes
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41. 41▶Randomized searching and sorting (Randomized algorithms, Fall 2022, Lecture 1)Randomized Algorithms, Fall 2022Notes
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42. 42▶CS 106L Fall 2019 - Lecture 3: Sequence Containers (Screencast)Stanford CS 106L Fall 2019 LecturesNotes
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43. 43▶CS 106L Fall 2019 - Lecture 3: Sequence Containers (Screencast)Stanford CS 106L Fall 2019 LecturesNotes
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44. 44▶CS 106L Fall 2019 - Lecture 4: Associative Containers (Screencast)Stanford CS 106L Fall 2019 LecturesNotes
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45. 45▶CS 106L Fall 2019 - Lecture 9: STL Summary (Screencast)Stanford CS 106L Fall 2019 LecturesNotes
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46. 46▶CS 106L Winter 2020 - Lecture 8: Functions and AlgorithmsStanford CS 106L Fall 2019 LecturesNotes
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47. 47▶CS 106L Fall 2019 - Lecture 6: Templates (Video)Stanford CS 106L Fall 2019 LecturesNotes
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48. 48▶CS 106L Fall 2019 - Lecture 5: Advanced Containers (Screencast)Stanford CS 106L Fall 2019 LecturesNotes
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49. 49▶CS 106L Fall 2019 - Lecture 2: Streams II (Screencast)Stanford CS 106L Fall 2019 LecturesNotes
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50. 50▶CS 106L Fall 2019 - Lecture 2: Streams II (Video)Stanford CS 106L Fall 2019 LecturesNotes
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