Linear Algebra, Calculus & Probability
The continuous math that powers graphics, ML and theory.
Calculus Essentials
Calculus is the mathematics of continuous change — and the hidden engine inside every gradient descent, physics simulation, probability distribution, and rendering algorithm in computer science.
Multivariable Calculus & Gradients
Gradients turn the geometry of multivariable functions into arrows that point uphill — and machine learning runs entirely on following them downhill.
Linear Algebra I: Vectors & Matrices
Vectors and matrices are the universal language of data transformation — master them and you hold the key to machine learning, graphics, physics simulation, and most of modern science.
Linear Algebra II: Eigenvalues & SVD
Eigenvalues and SVD are the X-ray vision of linear algebra — they reveal the hidden skeleton of any linear transformation.
Probability Theory
Probability theory is the mathematical language of uncertainty — the foundation beneath every algorithm that learns, infers, or decides under incomplete information.
Statistics & Inference
Statistics and inference transform raw data into justified beliefs — learning from noise to reason under uncertainty with mathematical precision.
Optimization & Gradient Descent
Optimization and gradient descent: the engine that trains every model you will ever build, rooted in the geometry of functions and the calculus of steepest descent.
Information Theory
Information theory reveals the fundamental limits of compression and communication by quantifying uncertainty as a precise mathematical quantity called entropy.