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Mathematics

Linear algebra, calculus, probability, and optimization foundations that power every machine learning algorithm.

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Foundations

(2 topics)

Vectors, Matrices, and Tensors

The fundamental data structures and linear algebra operations that power every Machine Learning model, Neural Network, and Large Language Model.

Matrix Multiplication and Linear Transformations

How matrices act as geometric functions that rotate, scale, project, and warp vector spaces—forming the mathematical backbone of every neural network layer.

Calculus

(2 topics)

Derivatives and Partial Derivatives

How calculus measures rates of change across millions of parameters—forming the mathematical compass behind Gradient Descent and Backpropagation.

The Chain Rule and Gradients

How the Chain Rule routes error signals backward through deep computational graphs and how Gradient Vectors guide multi-dimensional optimization.

Probability

(1 topic)

Probability, Distributions, and Bayes' Rule

How AI quantifies uncertainty, models data with probability distributions, updates beliefs via Bayes' Theorem, and trains neural networks using Maximum Likelihood Estimation.