Learn · Applied Linear Algebra in AI and ML

Lecture 34 of 60week-07

Lecture 35: Constrained Least Squares

Prof. Swanand Khare · IIT Kharagpur

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Applied Linear Algebra in AI and ML, IIT Kharagpur (at NPTEL)
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All the lectures

  1. Lecture 01 : Vector Spaces Video
  2. Lecture 02 : Vector Subspaces Video
  3. Lecture 03 : Linear Span and Linear Dependence Video
  4. Lecture 04 : Linear Independence Video
  5. Lecture 05 : Basis and Dimension Video
  6. Lecture 06 : Linear Functionals Video
  7. Lecture 07 : Norm of Vector Part - I Video
  8. Lecture 08 : Norm of Vector Part - II Video
  9. Lecture 09 : Linear Functions Video
  10. Lecture 10 : Affine Functions and Examples Video
  11. Lecture 11: Examples of Linear and Affine Functions Video
  12. Lecture 12: Function Composition Video
  13. Lecture 13: System of Linear Equations Video
  14. Lecture 14: Left Invertibility Video
  15. Lecture 15: Invertibility of Matrices Video
  16. Lecture 16: Triangular Systems Video
  17. Lecture 17: LU Decomposition Part - I Video
  18. Lecture 18: LU Decomposition Part - II Video
  19. Lecture 19: QR Decomposition (Rotators - Part I) Video
  20. Lecture 20: QR Decomposition (Rotators - Part II) Video
  21. Lecture 21: QR Decomposition (Reflectors - Part I) Video
  22. Lecture 22: QR Decomposition (Reflectors - Part II) Video
  23. Lecture 23: Matrix Norms Video
  24. Lecture 24: Sensitivity Analysis Video
  25. Lecture 25: Condition Number of a Matrix Video
  26. Lecture 26: Sensitivity Analysis -II Video
  27. Lecture 28: Least Squares - Part I Video
  28. Lecture 29: Least Squares - Part II Video
  29. Lecture 30: Least Squares - Part III Video
  30. Lecture 31: Least Squares Data Fitting Video
  31. Lecture 32: Examples of LS data fitting Video
  32. Lecture 33: Classification using Least Squares Video
  33. Lecture 34: Examples of LS classification Video
  34. Lecture 35: Constrained Least Squares Video
  35. Lecture 36: Multiobjective Least Squares Video
  36. Lecture 37: Eigenvalues and Eigenvectors (Part - I) Video
  37. Lecture 38: Eigenvalues and Eigenvectors (Part - II) Video
  38. Lecture 39: Spectral Decomposition Theorem Video
  39. Lecture 40: Positive Definite Matrices Video
  40. Lecture 27: Sensitivity Analysis -III Video
  41. Lecture 41 : Singular Value Decomposition (SVD) Video
  42. Lecture 42 : Proof of SVD Video
  43. Lecture 43 : Properties of SVD Video
  44. Lecture 44 : Another Proof of SVD Video
  45. Lecture 45 : Low Rank Approximations Video
  46. Lecture 46 : Principal Component Analysis Video
  47. Lecture 47 : SVD and Pseudo - Inverse Video
  48. Lecture 48 : SVD and the Least Squares Problem Video
  49. Lecture 49 : Sensitivity Analysis of the Least Squares Problem Video
  50. Lecture 50 : Power Method Video
  51. Lecture 51 : Directed Graphs and Properties Video
  52. Lecture 52 : Page Ranking Algorithm Video
  53. Lecture 53 : Inverse Eigen Value Problem Video
  54. Lecture 54 : Fastest Mixing Markov Chains on Graphs (Part I) Video
  55. Lecture 55 : Fastest Mixing Markov Chains on Graphs (Part II) Video
  56. Lecture 56: Sparse Solution and Underdetermined Systems Video
  57. Lecture 57: Structured Low Rank Approximations (Part I) Video
  58. Lecture 58: Structured Low Rank Approximations (Part II) Video
  59. Lecture 59: Structured Low Rank Approximations (Part III) Video
  60. Lecture 60: Recap Video