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Video course · 15 chapters · 365 min · certificate

Linear Algebra Course – Mathematics for Machine Learning and Generative AI

Linear algebra for machine learning and generative AI: a 2024 roadmap, prerequisites, norms and distances, vectors geometrically, special vectors, operations, length and dot product, the cosine rule and Cauchy-Schwarz, linear systems, matrices, Gaussian elimination and row echelon forms.

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What you'll learn

  • Follow a linear algebra roadmap for ML
  • Compute norms, distances and dot products
  • Work with vectors geometrically
  • Prove and apply Cauchy-Schwarz
  • Perform core matrix operations
  • Solve systems with Gaussian elimination and RREF

Chapters

15 chapters · 365:12
  1. 27:50 01Roadmap Members Introduction and roadmap Maths behind GenAI.
  2. 12:28 02Prereqs Members Prerequisites and real numbers What's assumed.
  3. 23:04 03Norms Members Norms, distance and vector foundations L2 and regularisation.
  4. 22:13 04Geometry Members Vectors geometrically Arrows.
  5. 24:50 05Special Members Special vectors and applications Unit, zero, sparse.
  6. 33:49 06Operations Members Vector operations and properties Element-wise.
  7. 25:00 07Advanced Members Advanced vector concepts Scalar multiplication.
  8. 30:46 08Dot product Members Length and dot product Pythagoras.
  9. 28:11 09Inequality Members Cosine rule and Cauchy-Schwarz Angles.
  10. 15:42 10Systems Members Introduction to linear systems Equations.
  11. 16:09 11Matrices Members Introduction to matrices m × n.
  12. 40:39 12Ops Members Core matrix operations Add, scale, multiply.
  13. 29:18 13Elimination Members Gaussian elimination Solve systems.
  14. 15:47 14Example Members Detailed example: solving systems Ax = b.
  15. 19:26 15RREF Members Augmented matrix, REF and RREF Row echelon forms.

Jobs this course suits

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Data Scientists