The complete mathematical foundation for Machine Learning, organized by chapters.
Scalars, Vectors, Matrices, and applications.
Vector transformation, Projection, etc.
Existence, Identity, and Eigen vectors.
Hyperplanes and geometric interpretation.
Mean, Median, Mode, Variance, and Standard Deviation.
Hypothesis Testing, p-values, and Confidence Intervals.
Conditional Probability, Bayes' Theorem, and Distributions.