These notes provide a handwritten, visual walkthrough of core linear algebra concepts and their direct applications in machine learning:
- Vectors & Matrices: Fundamental notation, matrix operations, transpose, dot products, determinants, and matrix inverses.
- Linear Systems & Elimination: Gaussian elimination, row operations, and Row Echelon Form (REF).
- Vector Spaces & Distance Metrics: Vector distances, $L_1$ and $L_2$ norms, orthogonality, and geometric projections.
- Machine Learning Applications: Best fit lines, linear regression formulations ($y = Wx + b$), weights and bias intuitions, single-layer perceptron representations, and dimensionality reduction.