Mathematical Foundations of Machine Learning
Explore the mathematical ideas behind machine learning, from linear algebra and probability to optimization.
Browse the notes View on GitHub
A place to build understanding
This site is a growing collection of notes, worked examples, and resources for studying the foundations of machine learning.
Topics to explore include:
- Linear algebra: vectors, matrices, eigenvalues, and decompositions.
- Calculus: derivatives, gradients, and multivariable functions.
- Probability and statistics: distributions, expectations, and inference.
- Optimization: objective functions, convexity, and gradient descent.
Use the sidebar to browse pages or search to find a topic.