Engineering reference for Indian students

Math & statistics

Linear Algebra for ML

5 self-contained study topics — notes, diagrams, formulas, and worked examples for exams and GATE.

Topics

Vectors and Matrices
For B.Tech exams, vectors and matrices is tested for definition plus one direct derivation or numerical; align notation with Goodfellow, Bengio & Courville (Deep Learning).
$Identity I; inverse AA^{-1} = I when det\ne 0$
Linear Transformations
For B.Tech exams, linear transformations is tested for definition plus one direct derivation or numerical; align notation with Tan, Steinbach & Kumar (Introduction to Data Mining).
Eigenvalues and Eigenvectors
For B.Tech exams, eigenvalues and eigenvectors is tested for definition plus one direct derivation or numerical; align notation with Tom Mitchell (Machine Learning).
Singular Value Decomposition
For B.Tech exams, singular value decomposition is tested for definition plus one direct derivation or numerical; align notation with Bishop (Pattern Recognition and Machine Learning).
$\sigma_{i}^{2} = eigenvalues of AᵀA$
Matrix Calculus Basics
For B.Tech exams, matrix calculus basics is tested for definition plus one direct derivation or numerical; align notation with Goodfellow, Bengio & Courville (Deep Learning).