Course Schedule — Fall 2026
The schedule below is subject to change as the semester progresses. Lecture topics may shift slightly depending on class pace.
Homework and exam dates are to remain fixed except in extraordinary circumstances.
| Week | Date | Lecture | Topic | Slides | Assignments / Notes | References |
|---|---|---|---|---|---|---|
| 1M | Mon, Aug 24 | L1 | Intro to CV & class | Lec01 | Classes start | |
| 1W | Wed, Aug 26 | L2 | What is an image? Intro to Python | Lec02 | Szeliski 1; Klette 1.1, 2.1 | |
| 2M | Mon, Aug 31 | L3 | Image Filtering | Lec03 | HW01 posted | Szeliski 3.2; Klette 2.2.2; Szeliski 3.4 |
| 2W | Wed, Sep 2 | L4 | Image Filtering | Lec03 | Last day to add/drop classes | |
| 3M | Mon, Sep 7 | — | No class — Labor Day | — | HW01 due; HW02 posted; Campus closed | |
| 3W | Wed, Sep 9 | L5 | Image Pyramids | Lec03, Lec04 | Census Day | Szeliski 3.5.2, 8.1.1; Klette 1.2 |
| 4M | Mon, Sep 14 | L6 | Fourier Transform | Lec04, Lec05 | HW02 due; HW03 posted | |
| 4W | Wed, Sep 16 | L7 | Fourier Transform | Lec05 | Career Day | Szeliski 4.2; Klette 2.3, 2.4 |
| 5M | Mon, Sep 21 | L8 | Edge Detection | Lec06 | HW03 due; HW04 posted | |
| 5W | Wed, Sep 23 | L9 | Edge Detection | Lec06, Lec07 | Exam 1 | Szeliski 4.1.2, 4.1.3, 4.3.2; Klette 2.3, 2.4, 9.2 |
| 6M | Mon, Sep 28 | L10 | Feature Points | Lec07 | HW04 due; HW05 posted | |
| 6W | Wed, Sep 30 | L11 | Feature Points & Matching | Lec07 | Szeliski 2.1 (esp. 2.1.5), 2.2, 2.3; Klette 6.1, 1.3 | |
| 7M | Mon, Oct 5 | L12 | Feature Matching | Lec07 | HW05 due; HW06 posted | |
| 7W | Wed, Oct 7 | L13 | Hough Transform | Lec08 | ||
| 8M | Mon, Oct 12 | — | No class — Fall Break | — | Fall Break: Oct 12–13 | |
| 8W | Wed, Oct 14 | L14 | Optical Flow, Color | Lec09, Lec10 | HW06 due; HW07 posted | Szeliski 2.1, 6.1, 6.2.1 |
| 9M | Mon, Oct 19 | L15 | Camera Geometry | Lec11 | Exam 2 | Szeliski 7, 8.1, 8.4, 11 |
| 9W | Wed, Oct 21 | L16 | Camera Geometry | Lec11 | HW07 due; HW08 posted | Szeliski 6.2.1 |
| 10M | Mon, Oct 26 | L17 | Camera Calibration | Lec12 | ||
| 10W | Wed, Oct 28 | L18 | Two-View Geometry | Lec13 | HW08 due; HW09 posted | Szeliski 5.3 |
| 11M | Mon, Nov 2 | L19 | Two-View Geometry | Lec13 | Szeliski 14 | |
| 11W | Wed, Nov 4 | L20 | Depth Cameras | Lec14 | HW09 due; HW10 posted | Szeliski 14.1, 14.2 |
| 12M | Mon, Nov 9 | L21 | Machine Learning | Lec15 | Exam 3 | Szeliski 14.5 |
| 12W | Wed, Nov 11 | L22 | Recognition | Lec16 | HW10 due | Goodfellow 6, 9 |
| 13M | Mon, Nov 16 | L23 | Face Detection, Pedestrian Detection | Lec17, Lec18 | HW11 posted | |
| 13W | Wed, Nov 18 | L24 | Neural Networks | Lec19 | Goodfellow 6, 7.1–7.5, 7.12 | |
| 14M | Mon, Nov 23 | L25 | Neural Networks | Lec19 | HW11 due | |
| 14W | Wed, Nov 25 | — | No class — Thanksgiving Break | — | Thanksgiving break | |
| 15M | Mon, Nov 30 | L26 | Training Neural Networks | Lec20 | HW12 posted | |
| 15W | Wed, Dec 2 | L27 | What do CNNs learn? | Lec21 | Exam 4 | |
| 16M | Mon, Dec 7 | L28 | What do CNNs learn? | Lec22 | HW12 due; Last week of class | |
| 16W | Wed, Dec 9 | L29 | CNN Architectures | Lec23 | Last week of class; Classes end |