Mathematical Foundations of Machine Learning

The Mathematical Foundations of Machine Learning Training is a technical program for participants who want to understand machine learning from the math up. It opens with vectors, norms, dot products and angle calculations and shows how vectors are used in feature representation and similarity measures. Matrix operations together with eigenvalues and eigenvectors then carry the discussion into data transformations, PCA and the derivation of linear regression. The probability section covers random variables, distributions, conditional probability, Bayes' theorem, data preprocessing and feature engineering from a mathematical perspective. Loss functions, gradient descent and convergence analysis follow, and the program closes with logistic regression, linear discriminant analysis, Naive Bayes and Gaussian Mixture Models as the classification and generative modeling block.