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Lecture 26 of 70Week 6

Lecture 13(B) : Support Vector Machine (part - 2)

Prof. Krishanu Biswas and Prof. Pratik Kumar Ray · IIT Kanpur / IIT Ropar

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Introductory Materials Informatics, IIT Kanpur / IIT Ropar (at NPTEL)
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Kanpur.
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All the lectures

  1. Lecture 1(A) : General Introduction (Part 1) Video
  2. Lecture 1(B) : General Introduction (Part 2) Video
  3. Lecture 2(A) : Statistics & Regression (Part 1) Video
  4. Lecture 2(B) : Statistics & Regression (Part 2) Video
  5. Lecture 3(A) : Probability & Classification (Part 1) Video
  6. Lecture 3(B) : (Part - 2) Probability & Classification Video
  7. Lecture 4(A) : (Part-1) Data Handling - I Video
  8. Lecture 4(B) : (Part-2) Data Handling - I Video
  9. Lecture 5(A) : (Part-1) Data Handling - II Video
  10. Lecture 5(B) : (Part-2) Data Handling - II Video
  11. Lecture 6(A) : Materials Informatics in Action (Part - 1) Video
  12. Lecture 6(B) : Materials Informatics in Action (Part - 2) Video
  13. Lecture 7(A) : The Gradient Descent Methods (Part - 1) Video
  14. Lecture 7(B) : The Gradient Descent Methods (Part - 2) Video
  15. Lecture 8(A) : Regularization and solvers in regression Video
  16. Lecture 8(B) : Regularization and solvers in regression Video
  17. Lecture 9(A) : Various Types of Machine Learning (Part-1) Video
  18. Lecture 9(B) : Various Types of Machine Learning (Part-2) Video
  19. Lecture 10(A) : Describing the Problems (Part-1) Video
  20. Lecture 10(B) : Describing the Problems (Part-2) Video
  21. L11(A) : Linear Models (Part-1) Video
  22. L11(B) : Linear Models (Part-2) Video
  23. L12(A) : Decisions Tree (Part-1) Video
  24. L12(B) : Decisions Tree (Part-2) Video
  25. L13(A) : Decision Trees (Part-1) Video
  26. Lecture 13(B) : Support Vector Machine (part - 2) Video
  27. Lecture 14(A) : Support Vector Machine (part -1) Video
  28. Lecture 14(B) : Support Vector Machine and Neural Networks (part - 2) Video
  29. Lecture 15(A) : Neural Networks (part -1) Video
  30. Lecture 15(B) : Neural Networks (part - 2) Video
  31. Lecture 16(A) : Clustering (Part - 1) Video
  32. Lecture 16(B) : Clustering (Part - 2) Video
  33. Lecture 17(A) : Model Selection (Part - 1) Video
  34. Lecture 17(B) : Clustering (Part - 2) Video
  35. Lecture 18(A) : Visualization (Part - 1) Video
  36. Lecture 18(B) : Visualization and transformation (Part - 2) Video
  37. Lecture 19(A) - (part 1): Dataset transformations Video
  38. Lecture 19(B) - (part 2): Dataset transformations Video
  39. Lecture 20(A) - (part 1): Data mining Video
  40. Lecture 20(B) - (part 2): Mapping of materials Video
  41. Lecture 21(A) - (part 1) : Data in Materials Informatics Video
  42. Lecture 21(B) - (part 2) : Data in Materials Informatics Video
  43. Lecture 22(A) _ (part 1) : Structure Maps Video
  44. Lecture 22(B) - (part 2) : Structure Maps Video
  45. Lecture -23 (Part 1) - Phase Diagrams Video
  46. Lecture -23 (Part 2) - Periodic Table and Elemental Descriptors Video
  47. Lecture -24 (Part 1) - Featurization - Physical Principles Video
  48. Lecture -24 (Part 2) - Featurization - Pair Plots and Correlation Matrix Video
  49. Lecture 25 - Thermodynamic Features - Miedema’s Model Video
  50. Lecture 26 - ( part 1) : Structure of Materials Video
  51. Lecture -26 (part 2): Structure of Materials - Microstructure Video
  52. Lecture -27 (part 1): Prediction of Composition Based Properties - I Video
  53. Lecture -27 (part 2): Prediction of Composition Based Properties - II Video
  54. Lec-28 (part 1): Molecular Fingerprints - I Video
  55. Lec-28 (part 2): Molecular Fingerprints - II Video
  56. Lecture -29 (part 1): Mathematical Microstructure Video
  57. Lecture -29 (part 2): The Microstructure Function Video
  58. Lecture 30 - Two-point Statistics and Dimensionality Reduction Video
  59. Lecture 31 A - Combinatorial-Materials-Science Video
  60. Lecture 31 B - Convolutional Neural Network Video
  61. Lecture 32 - Microstructure Representation-Synthetic Microstructures Video
  62. Lecture 33 - P-S-P Linkage Video
  63. Lecture 34 - Decision Making and Interpretation Video
  64. Lecture 35 Part 1: (Setting up ML problems) Video
  65. Lecture 35 Part 2: (Interrogating Machine Learning models - Examples) Video
  66. Lecture 36: (Physically Informed Neural Networks) Video
  67. Lecture 37: (Combinatorial Materials Processing) Video
  68. Lecture 38: (High throughput Characterization) Video
  69. Lecture 39: (Synthetic Data) Video
  70. Lecture 40: (Conclusion, Challenges and Future Directions) Video