Mathematics for Machine Learning
Build the mathematical foundation essential for understanding ML algorithms. Strengthen your mathematical foundations specifically for machine learning.
Level: Beginner · Category: Mathematics · Estimated time: 7 hours
Lessons
- Vectors & Vector Spaces — Vectors, dot products, norms, projections, and vector spaces.
- Matrices & Linear Transformations — Matrix operations, transformations, determinants, and rank.
- Eigenvalues & Eigenvectors — Eigendecomposition, PCA intuition, and spectral methods.
- Calculus for Optimization — Derivatives, partial derivatives, gradients, and the chain rule.
- Probability Fundamentals — Probability axioms, distributions, expectation, variance, and Bayes' theorem.
- Statistics for ML — Hypothesis testing, confidence intervals, MLE, and MAP estimation.
- Information Theory — Entropy, cross-entropy, KL divergence — the math behind loss functions.
Topics covered
linear-algebra, calculus, probability, statistics, optimization