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