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Vector Spaces, Linear Independence, Linear Transformations, Matrix Operations, Eigenvectors, Covariance Matrix, PCA, Kernel trick, Eigenfaces, Cluster Analysis

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Linear Algebra

In this we cover the essence of linear algebra:

  • Vector Spaces, Basis, Origin, Span
  • Linear Independence, Linear Combination
  • Matrix Operations (Add, Subtract, Multiply, Transpose, Inverse, Determinant)
  • Vectorization in numpy
  • Linear Transformations (Nullspace, Rank)
  • Eigenvectors & Eigenvalues
  • Centroid & Covariance Matrix

And some introduction to:

  • Images represented as matrices
  • Affine transformation, Homography
  • Cover's theorem (kernel trick)
  • Principal Component Analysis
  • Eigenfaces
  • Cluster Analysis
  • K-Means Clustering

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Vector Spaces, Linear Independence, Linear Transformations, Matrix Operations, Eigenvectors, Covariance Matrix, PCA, Kernel trick, Eigenfaces, Cluster Analysis

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