Part I — Linear Algebra
Notes on Chapter 2, Linear Algebra, of Mathematics for Machine Learning.
Chapter 2 builds the vocabulary that the rest of the book leans on: systems of linear equations, matrices, solving systems, vector spaces, linear independence, bases and rank, linear mappings and affine spaces. It closes with further reading; norms, inner products and orthogonality — the tools of Chapter 3’s analytic geometry — come next.
Notes in this part
| Section | Note | Status |
|---|---|---|
| 2.1 Systems of Linear Equations | 2.1 Systems of Linear Equations | done |
| 2.2 Matrices | — | not started |
| 2.3 Solving Systems of Linear Equations | — | not started |
| 2.4 Vector Spaces | — | not started |
| 2.5 Linear Independence | — | not started |
| 2.6 Basis and Rank | — | not started |
| 2.7 Linear Mappings | — | not started |
| 2.8 Affine Spaces | — | not started |
| 2.9 Further Reading | — | not started |
The authoritative, machine-readable version of this table is docs/state.md in the
repository. Section titles were checked against the book’s own table of contents
(2024 free PDF at mml-book.com): Chapter 2 runs 2.1–2.8, and 2.9 is “Further
Reading” — there is no affine-mappings section in this chapter. The prose above
mentions affine mappings only because they appear later, in the context of affine
spaces.
A note on the ordering
Notes follow the book’s own section numbering rather than a reading order chosen here, so a note for Section 2.7 can be added before Section 2.5 without renumbering anything. The first note was prompted by a question about the production-plan model in Example 2.1, which is why Chapter 1 (Introduction and Motivation) has no notes yet.