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  • Lesson.No : 39
  • 00:12:49

  • Eigendecomposition — Topic 33 of Machine Learning Foundations

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Course Lessons

  1. 1- Machine Learning Foundations: Welcome to the Journey
  2. 2- What Linear Algebra Is — Topic 1 of Machine Learning Foundations
  3. 3- Plotting a System of Linear Equations — Machine Learning Foundations Bonus Video
  4. 4- Linear Algebra Exercise — Topic 2 of Machine Learning Foundations
  5. 5- Tensors — Topic 3 of Machine Learning Foundations
  6. 6- Scalars — Topic 4 of Machine Learning Foundations
  7. 7- Vectors and Vector Transposition — Topic 5 of Machine Learning Foundations
  8. 8- Norms and Unit Vectors — Topic 6 of Machine Learning Foundations
  9. 9- Basis, Orthogonal, and Orthonormal Vectors — Topic 7 of Machine Learning Foundations
  10. 10- Matrix Tensors — Topic 8 of Machine Learning Foundations
  11. 11- Generic Tensor Notation — Topic 9 of Machine Learning Foundations
  12. 12- Exercises on Algebra Data Structures — Topic 10 of Machine Learning Foundations
  13. 13- Tensor Operations — Segment 2 of Subject 1, "Intro to Linear Algebra", ML Foundations
  14. 14- Tensor Transposition — Topic 11 of Machine Learning Foundations
  15. 15- Basic Tensor Arithmetic (The Hadamard Product) — Topic 12 of Machine Learning Foundations
  16. 16- Tensor Reduction — Topic 13 of Machine Learning Foundations
  17. 17- The Dot Product — Topic 14 of Machine Learning Foundations
  18. 18- Exercises on Tensor Operations — Topic 15 of Machine Learning Foundations
  19. 19- Solving Linear Systems with Substitution — Topic 16 of Machine Learning Foundations
  20. 20- Solving Linear Systems with Elimination — Topic 17 of Machine Learning Foundations
  21. 21- Visualizing Linear Systems — Machine Learning Foundations Bonus Video
  22. 22- Matrix Properties — Final Segment of Subject 1, "Intro to Linear Algebra", ML Foundations
  23. 23- The Frobenius Norm — Topic 18 of Machine Learning Foundations
  24. 24- Matrix Multiplication — Topic 19 of Machine Learning Foundations
  25. 25- Symmetric and Identity Matrices — Topic 20 of Machine Learning Foundations
  26. 26- Matrix Multiplication Exercises — Topic 21 of Machine Learning Foundations
  27. 27- Matrix Inversion — Topic 22 of Machine Learning Foundations
  28. 28- Diagonal Matrices — Topic 23 of Machine Learning Foundations
  29. 29- Orthogonal Matrices — Topic 24 of Machine Learning Foundations
  30. 30- Orthogonal Matrix Exercises — Topic 25 of Machine Learning Foundations
  31. 31- Linear Algebra II: Matrix Operations — Subject 2 of Machine Learning Foundations
  32. 32- Applying Matrices — Topic 26 of Machine Learning Foundations
  33. 33- Affine Transformations — Topic 27 of Machine Learning Foundations
  34. 34- Eigenvectors and Eigenvalues — Topic 28 of Machine Learning Foundations
  35. 35- Matrix Determinants — Topic 29 of Machine Learning Foundations
  36. 36- Determinants of Larger Matrices — Topic 30 of Machine Learning Foundations
  37. 37- Determinant Exercises — Topic 31 of Machine Learning Foundations
  38. 38- Determinants and Eigenvalues — Topic 32 of Machine Learning Foundations
  39. 39- Eigendecomposition — Topic 33 of Machine Learning Foundations
  40. 40- Eigenvector and Eigenvalue Applications — Topic 34 of Machine Learning Foundations
  41. 41- Matrix Operations for Machine Learning — Final Segment of Subject 2, "Linear Algebra II"
  42. 42- Singular Value Decomposition — Topic 35 of Machine Learning Foundations
  43. 43- Data Compression with SVD — Topic 36 of Machine Learning Foundations
  44. 44- The Moore-Penrose Pseudoinverse — Topic 37 of Machine Learning Foundations
  45. 45- Regression with the Pseudoinverse — Topic 38 of Machine Learning Foundations
  46. 46- The Trace Operator — Topic 39 of Machine Learning Foundations
  47. 47- Principal Component Analysis (PCA) — Topic 40 of Machine Learning Foundations
  48. 48- Linear Algebra Resources — Topic 41 of Machine Learning Foundations