Week 4 — Linear algebra + regression

Milestone

Build regression and PCA modules for market data analysis.

Success criteria

Can run linear regression and PCA on multi-asset returns and explain the output in a notebook.

Deliverables

• [ ] Solve exercises on matrices and least squares.
• [ ] Implement linear regression manually and with NumPy.
• [ ] Implement PCA using eigenvectors/SVD.
• [ ] Run PCA on multiple asset returns.
• [ ] Document what PCA means in risk/factor analysis.

Books

• Schaum's Outline of Linear Algebra — Exercises for vectors, matrices, eigenvalues, least squares, PCA foundations.
• Schaum's Outline of Probability and Statistics — Exercises for probability, statistics, distributions, covariance, regression.

Retrospective

Fill this in during the Sunday review.

What did I finish?

What is blocked?

What confused me?

Key takeaway this week: