Readings refer to chapters of Foundations of Computational Imaging. Homework and labs are due at the start of class on the day listed. Laboratory assignments are on the lab web page.
| # | Date | Reading Due | HW Due | Lab Due | Topic |
|---|---|---|---|---|---|
| Week 1: Aug. 24–28 | |||||
| 1 | Aug. 24 | Chapter 1 | What is computational imaging? | ||
| 2 | Aug. 26 | Chapter 2.1 | Random variables and expectation | ||
| 3 | Aug. 28 | Chapter 2.2 | HW 1 | Lab 1 | Commonly used distributions |
| Week 2: Aug. 31–Sept. 4 | |||||
| 4 | Aug. 31 | Chapter 2.3 | Frequentist and Bayesian estimators | ||
| 5 | Sept. 2 | Chapter 2.4 | Discrete-time random processes | ||
| 6 | Sept. 4 | Chapter 3.1–3.2 | HW 2 | Lab 2 | Causal prediction in Gaussian models |
| Week 3: Sept. 7–11 | |||||
| Monday Sept. 7: Labor Day, no class | |||||
| 7 | Sept. 9 | Chapter 3.3 | 1-D Gaussian autoregressive models | ||
| 8 | Sept. 11 | Chapter 3.4 | HW 3 | 2-D Gaussian AR models | |
| Week 4: Sept. 14–18 | |||||
| 9 | Sept. 14 | Chapter 4.1–4.2 | Noncausal prediction in Gaussian models | ||
| 10 | Sept. 16 | Chapter 4.3–4.4 | 1-D and 2-D Gaussian Markov random fields | ||
| 11 | Sept. 18 | Chapter 4.5–4.6 | HW 4 | Lab 3 | GMRF vs. AR models |
| Week 5: Sept. 21–25 | |||||
| 12 | Sept. 21 | Chapter 5.1–5.2 | MAP image restoration; computing the MAP estimate | ||
| 13 | Sept. 23 | Chapter 5.3 | Gradient descent optimization | ||
| 14 | Sept. 25 | Chapter 5.4–5.5 | HW 5 | ICD, preconditioning, and conjugate gradient | |
| Week 6: Sept. 28–Oct. 2 | |||||
| 15 | Sept. 28 | Chapter 6.1–6.2 | Non-Gaussian MRFs, potential and influence functions | ||
| 16 | Sept. 30 | Chapter 6.3–6.4 | Convex potentials and the scale parameter | ||
| 17 | Oct. 2 | Chapter 7 | HW 6 | Lab 4 | MAP estimation with non-Gaussian priors |
| Week 7: Oct. 5–9 | |||||
| 18 | Oct. 5 | Chapter 8.1–8.2 | Surrogate functions and the MM algorithm | ||
| 19 | Oct. 7 | Chapter 8.2–8.3 | Surrogate properties, and building the quadratic surrogate | ||
| 20 | Oct. 9 | Chapter 8.3–8.4 | HW 7 | Reweighting, and the one-step optimal line search | |
| Week 8: Oct. 12–16 | |||||
| Oct. 12–13: Fall break, no class | |||||
| 21 | Oct. 14 | Chapter 9.1–9.2 | Constrained optimization; Lagrangian duality | ||
| 22 | Oct. 16 | Chapter 9.3 | The augmented Lagrangian | ||
| Week 9: Oct. 19–23 | |||||
| 23 | Oct. 19 | Chapter 9.4 | Proximal maps and shrinkage | ||
| 24 | Oct. 21 | Chapter 9.5 | Variable splitting | ||
| 25 | Oct. 23 | Chapter 9.5 | HW 8 | Lab 5 | The ADMM algorithm |
| Week 10: Oct. 26–30 | |||||
| 26 | Oct. 26 | Chapter 10.1–10.2 | Plug-and-play: motivation; plug-in denoisers | ||
| 27 | Oct. 28 | Chapter 10.3 | Consensus equilibrium for two models | ||
| 28 | Oct. 30 | Chapter 10.4 | HW 9 | Multiagent consensus equilibrium | |
| Week 11: Nov. 2–6 | |||||
| 29 | Nov. 2 | Chapter 12.1–12.2 | EM: motivation; Gaussian mixtures | ||
| 30 | Nov. 4 | Chapter 12.3–12.4 | EM theory; EM for Gaussian mixtures | ||
| 31 | Nov. 6 | MIDTERM EXAM — in class | |||
| Week 12: Nov. 9–13 | |||||
| 32 | Nov. 9 | Chapter 12.5–12.6 | EM clustering; convergence and majorization | ||
| 33 | Nov. 11 | Chapter 12.7 | Simplified EM update derivations | ||
| 34 | Nov. 13 | Chapter 13.1–13.2 | HW 10 | Lab 6 | Markov chains and their estimation |
| Week 13: Nov. 16–20 | |||||
| 35 | Nov. 16 | Chapter 13.3 | Hidden Markov models (highlights) | ||
| 36 | Nov. 18 | Chapter 13.4 | Stationary distributions of Markov chains | ||
| 37 | Nov. 20 | Chapter 13.5 | HW 11 | Properties of Markov chains | |
| Week 14: Nov. 23–27 | |||||
| 38 | Nov. 23 | Chapter 14.1–14.4 | MRFs and Gibbs distributions; the Ising model (highlights) | ||
| Nov. 25–28: Thanksgiving, no class | |||||
| Week 15: Nov. 30–Dec. 4 | |||||
| 39 | Nov. 30 | Chapter 15.1–15.2 | Simulation; the Metropolis sampler | ||
| 40 | Dec. 2 | Chapter 15.3–15.4 | Hastings–Metropolis; sampling of MRFs | ||
| 41 | Dec. 4 | Chapter 15.5 | HW 12 | Lab 7 | The Gibbs sampler |
| Week 16: Dec. 7–11 | |||||
| 42 | Dec. 7 | Course review and final-exam preparation | |||
| 43 | Dec. 9 | To be determined | |||
| 44 | Dec. 11 | To be determined | |||
| Final exam: during finals week, Dec. 14–18. Time and location will be announced by the Registrar. | |||||