Graduate-level treatment of the mathematical foundations underlying modern machine learning algorithms. Covers theory with hands-on Python implementations.
| Module | Content |
|---|---|
| Linear Algebra | Vector spaces, eigendecomposition, SVD, PCA |
| Calculus | Gradients, Jacobians, Hessians, chain rule |
| Probability & Stats | Distributions, Bayes theorem, MLE, MAP |
| Optimization | Gradient descent, convexity, Lagrange multipliers |
| Information Theory | Entropy, KL divergence, mutual information |
Maestría en Inteligencia Artificial · Universidad Politécnica Metropolitana de Hidalgo