analysis lecture

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1. 1▶Shape analysis, lecture 18: Clustering and segmentationMIT 6.838 | Shape Analysis | Spring 2017Notes
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2. 2▶Shape analysis, lecture 17: Optimal transportMIT 6.838 | Shape Analysis | Spring 2017Notes
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3. 3▶Shape analysis, lecture 16: DEC II, discrete vector fields, other topicsMIT 6.838 | Shape Analysis | Spring 2017Notes
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4. 4▶Shape analysis, lecture 15: Exterior calculus II, discrete exterior calculus IMIT 6.838 | Shape Analysis | Spring 2017Notes
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5. 5▶Shape analysis, lecture 13: Laplacian IV (applications)MIT 6.838 | Shape Analysis | Spring 2017Notes
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6. 6▶Shape analysis, lecture 12: Laplacian III (discretization)MIT 6.838 | Shape Analysis | Spring 2017Notes
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7. 7▶Shape analysis, lecture 10: Laplacian IIMIT 6.838 | Shape Analysis | Spring 2017Notes
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8. 8▶Shape analysis, lecture 9: Embedding II, Laplacian IMIT 6.838 | Shape Analysis | Spring 2017Notes
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9. 9▶Shape analysis, lecture 8: Geodesics III, introduction to embeddingMIT 6.838 | Shape Analysis | Spring 2017Notes
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10. 10▶Shape analysis, lecture 7: Geodesics IIMIT 6.838 | Shape Analysis | Spring 2017Notes
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11. 11▶Math 139 Fourier Analysis Lecture 10.1 L^2 convergence of Fourier SeriesCourse 8: Fourier AnalysisNotes
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12. 12▶Math 139 Fourier Analysis Lecture 11.1: Fourier series need not converge at point of continuityCourse 8: Fourier AnalysisNotes
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13. 13▶Math 139 Fourier Analysis Lecture 03: Introduction to Fourier SeriesCourse 8: Fourier AnalysisNotes
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14. 14▶Math 139 Fourier Analysis Lecture 09: L^2 convergence of the Fourier SeriesCourse 8: Fourier AnalysisNotes
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15. 15▶Math 139 Fourier Analysis Lecture 12: Fourier series and the isoperimetric inequalityCourse 8: Fourier AnalysisNotes
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16. 16▶Math 139 Fourier Analysis Lecture 14: A continuous, nowhere differentiable functionCourse 8: Fourier AnalysisNotes
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17. 17▶Math 139 Fourier Analysis Lecture 13: Weyl's equidistribution theoremCourse 8: Fourier AnalysisNotes
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18. 18▶Math 139 Fourier Analysis Lecture 17: Fourier InversionCourse 8: Fourier AnalysisNotes
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19. 19▶Math 139 Fourier Analysis Lecture 15: The Fourier TransformCourse 8: Fourier AnalysisNotes
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20. 20▶Math 139 Fourier Analysis Lecture 16: Basic Properties of the Fourier TransformCourse 8: Fourier AnalysisNotes
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21. 21▶Beyond Worst-Case Analysis (Lecture 15: Smoothed Complexity and Pseudopolynomial-Time Algorithms)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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22. 22▶Beyond Worst-Case Analysis (Lecture 14: Smoothed Analysis of Pareto Curves)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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23. 23▶Beyond Worst-Case Analysis (Lecture 13: Smoothed Analysis of Local Search)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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24. 24▶Beyond Worst-Case Analysis (Lecture 12: LP Decoding/Introduction to Smoothed Analysis)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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25. 25▶Beyond Worst-Case Analysis (Lecture 11: LP Decoding)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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26. 26▶Beyond Worst-Case Analysis (Lecture 10: Planted and Semi-Random Graph Models)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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27. 27▶Beyond Worst-Case Analysis (Lecture 9: A Taste of Compressive Sensing)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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28. 28▶Beyond Worst-Case Analysis (Lecture 8: Exact Recovery in Stable Cut Instances)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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29. 29▶Beyond Worst-Case Analysis (Lecture 7: Perturbation Stability and Single-Link++)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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30. 30▶Beyond Worst-Case Analysis (Lecture 6: Clustering in Approximation-Stable Instances)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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31. 31▶Beyond Worst-Case Analysis (Lecture 5: Computing Independent Sets:A Parameterized Analysis)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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32. 32▶Beyond Worst-Case Analysis (Lecture 4: Parameterized Analysis of Online Paging)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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33. 33▶Beyond Worst-Case Analysis (Lecture 3: Online Paging and Resource Augmentation)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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34. 34▶Beyond Worst-Case Analysis (Lecture 2: Instance-Optimal Geometric Algorithms)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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35. 35▶Beyond Worst-Case Analysis (Lecture 1: Three Motivating Examples)Beyond Worst-Case Analysis (Stanford CS264, Fall 2014)Notes
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36. 36▶Real Analysis, Lecture 15: Convergence of SequencesReal AnalysisNotes
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37. 37▶Real Analysis, Lecture 14: Connected Sets, Cantor SetsReal AnalysisNotes
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38. 38▶Real Analysis, Lecture 13: Compactness and the Heine-Borel TheoremReal AnalysisNotes
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39. 39▶Real Analysis, Lecture 12: Relationship of Compact Sets to Closed SetsReal AnalysisNotes
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40. 40▶Real Analysis, Lecture 11: Compact SetsReal AnalysisNotes
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41. 41▶Real Analysis, Lecture 10: The Relationship Between Open and Closed SetsReal AnalysisNotes
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42. 42▶Real Analysis, Lecture 9: Limit PointsReal AnalysisNotes
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43. 43▶Real Analysis, Lecture 8: Cantor Diagonalization and Metric SpacesReal AnalysisNotes
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44. 44▶Real Analysis, Lecture 7: Countable and Uncountable SetsReal AnalysisNotes
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45. 45▶Real Analysis, Lecture 6: Principle of InductionReal AnalysisNotes
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