Beginner → Intermediate
Linear Algebra & Optimization for Quants
The other half of quant math — vectors, matrices, eigenvalues, SVD, PCA, least-squares regression, and convex optimization — built to where you can extract PCA factors and solve a Markowitz portfolio by hand.
📚 18 lessons⏱ ~2.4 hours∑ Server-rendered mathematics
All-access membership — $29/mo
Unlocks this and every other course.
What you'll learn
- ✓Compute with vectors and matrices, solve linear systems, and reason about rank and invertibility
- ✓Find eigenvalues and eigenvectors, diagonalize, and test matrices for positive definiteness
- ✓Build covariance matrices and extract principal components (PCA) from real return data
- ✓Fit least-squares regressions and estimate factor/CAPM betas from the normal equations
- ✓Set up and solve constrained optimization, including the mean-variance (Markowitz) portfolio
Use the outline on the left to navigate — or press ⌘K to jump to any lesson.