Linear Algebra for Machine Learning

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Section 1: Introduction

  1. Why Learn Linear Algebra?
  2. Download Resources
  3. Types of Tensors
  4. Scalars
  5. Vectors
  6. L1 and L2 Norm
  7. Angle between Vectors
  8. Projections and Unit Vector
  9. Basis, Orthogoanal and Orthonormal Vectors

Section 2: Matrices

  1. Types of Matrices
  2. Matrix Operations
  3. Orthogonal and Orthonormal Matrices

Section 3: Essential Equations

  1. Equation of line, plane and hyperplane
  2. Geometric interpretation of Weights vector
  3. Equation of Circle, Sphere and Hyperplane

Section 4: Towards SVD and PCA

  1. Eigenvalues and Eigenvectors
  2. Eigen Decomposition
  3. Singular Value Decomposition (SVD)
  4. Principal Component Analysis (PCA)
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