Skip to main content

Someone wanted to learn this too, so Grasp built them a personal learning path.

You can keep exploring this one or create a path like this one shaped around you.

Deep math for machine learning

12 Modules · 87 Lessons

Recommended resources

Module 2

Linear Transformations and Matrices

Module 3

Systems of Linear Equations and Invertibility

Module 4

Determinants

Module 5

Inner Product Spaces and Orthogonality

Module 6

Eigenvalues and Diagonalization

Module 7

Spectral Theory and Singular Value Decomposition (SVD)

Module 8

Application: Principal Component Analysis (PCA)

Module 9

Foundations of Probability

Module 10

Random Variables and Distributions

Module 11

Joint Distributions and Limit Theorems

Module 12

Statistical Estimation for Machine Learning