| Week | Main Topic | Instructor |
| 1 | Chapter 1: overview, vector spaces, vector and matrix norms | Gobbert |
| 2 | Chapter 2: Gaussian elimination, fundamentals and examples | Gobbert |
| 3 | Chapter 2: Gaussian elimination, error analysis and pivoting | Gobbert |
| 4 | Chapter 2: Gaussian elimination, practical issues | Gobbert |
| 5 | Chapter 2: other methods for special matrices (s.p.d., banded, sparse) | Gobbert |
| 6 | Chapter 3: least squares problems, problem and methods | Gobbert |
| 7 | Chapter 3: orthogonal matrices, practical issues | Kuang |
| 8 | Chapter 4: nonsymmetric eigenvalue problems, introduction and example | Kuang |
| 9 | Chapter 4: nonsymmetric eigenvalue problems, methods | Kuang |
| 10 | Chapter 5: symmetric eigenvalue problems, singular value decomposition | Kuang |
| 11 | Chapter 6: iterative methods, application example | Kuang |
| 12 | Chapter 6: iterative methods, basic methods | Kuang |
| 13 | Chapter 6: conjugate gradient and Krylov subspace methods | Gobbert |
| 14 | review and catch-up | Gobbert/Kuang |