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# CSE 559A: Computer Vision
## Course Description
## Course Overview
This course introduces computational systems that analyze images and infer physical structure, objects, and scenes. Topics include color, shape, geometry, motion estimation, classification, segmentation, detection, restoration, enhancement, and synthesis. Emphasis is on mathematical foundations, geometric reasoning, and deep-learning approaches.
**Department:** Computer Science & Engineering (559A)
**Credits:** 3
**Time:** Tuesday/Thursday 1–2:20pm
**Location:** Jubel Hall 120
**Modality:** In-person
**Instructor:** Prof. Nathan Jacobs
**Email:** [jacobsn@wustl.edu](mailto:jacobsn@wustl.edu) (Piazza preferred)
**Office:** McKelvey Hall 3032 or Zoom
**Office Hours:** By appointment
**TAs:** Nia Hodges, Dijkstra Liu, Alex Wollam, David Wang
Office hours posted on Canvas.
### Textbook
Primary: *Computer Vision: Algorithms and Applications (2nd Ed.)* — [http://szeliski.org/Book/](http://szeliski.org/Book/)
Secondary: *Computer Vision: Models, Learning, and Inference* — [http://www.computervisionmodels.com/](http://www.computervisionmodels.com/)
Secondary: *Foundations of Computer Vision* (MIT Press)
## Prerequisites
**Official:** CSE 417T, ESE 417, CSE 514A, or CSE 517A
**Practical:** Python programming, data structures, and strong background in linear algebra, vector calculus, and probability.
## Learning Outcomes
Students completing the course will be able to:
* Describe the image formation process mathematically.
* Compare classical and modern approaches to geometry, motion, detection, and semantic tasks.
* Derive algorithms for vision problems using mathematical tools.
* Implement geometric and semantic inference systems in Python.
## Course Topics
* **Low-Level Feature Extraction:** classical and modern (CNNs/Transformers)
* **Semantic Vision:** classification, segmentation, detection
* **Geometric Vision:** image formation, transformations, motion, metrology, stereo, depth
* **Extended Topics:** e.g., generative models, multimodal learning (TBD)
---
## Grading
### Homework
Homework consists of ~7 programming assignments in Python, focused on implementing core algorithms. Most include auto-graded components. Two late days allowed per assignment; after that, the score is zero. No late submissions for quizzes, paper reviews, or project.
### Exams / Quizzes
There are ~5 quizzes covering lectures and readings. They include both theoretical and applied questions. No late quizzes.
### Paper Reviews
Four short reviews of recent computer vision research papers. Includes an in-class discussion component.
### Project
An individual or small-team project implementing, evaluating, or developing a vision method. Specifications on Canvas.
### Final Grades
| Component | Weight |
| ----------------- | ------ |
| Homework (~7) | ~60% |
| Quizzes (~5) | ~15% |
| Paper Reviews (4) | ~5% |
| Project | ~15% |
| Participation | ~5% |
### Grading Scale
| Letter | Range |
| ------ | ------------- |
| A | 94% and above |
| A- | <94% to 90% |
| B+ | <90% to 87% |
| B | <87% to 84% |
| B- | <84% to 80% |
| C+ | <80% to 77% |
| C | <77% to 74% |
| C- | <74% to 70% |
| D+ | <70% to 67% |
| D | <67% to 64% |
| D- | <64% to 61% |
| F | <61% |
---
## Schedule
Approximate; see Canvas for updates.
| Week | Date | Topic | Notes |
| ------------ | ------ | -------------------------------------- | ----------------------------------------- |
| W1 | Jan 14 | Overview | |
| | Jan 16 | Image Formation & Filtering | |
| W2 | Jan 21 | Image Formation & Filtering | HW0 due |
| | Jan 23 | Image Formation & Filtering | |
| W3 | Jan 28 | Image Formation & Filtering | HW1 due |
| | Jan 30 | Image Formation & Filtering | Module Quiz |
| W4 | Feb 4 | Deep Learning for Image Classification | Paper review due |
| | Feb 6 | Deep Learning for Image Classification | |
| W5 | Feb 11 | Deep Learning for Image Classification | HW2 due |
| | Feb 13 | Deep Learning for Image Classification | |
| W6 | Feb 18 | Deep Learning for Image Classification | HW3 due; paper review due |
| | Feb 20 | Deep Learning for Image Classification | |
| W7 | Feb 25 | Deep Learning for Image Classification | Module Quiz |
| | Feb 27 | Deep Learning for Image Understanding | |
| W8 | Mar 4 | Deep Learning for Image Understanding | HW4 due; paper review due |
| | Mar 6 | Deep Learning for Image Understanding | Module Quiz |
| Spring Break | — | — | |
| W9 | Mar 18 | Feature Detection, Matching, Motion | |
| | Mar 20 | Feature Detection, Matching, Motion | |
| W10 | Mar 25 | Feature Detection, Matching, Motion | HW5 due; project launch; paper review due |
| | Mar 27 | Feature Detection, Matching, Motion | Module Quiz |
| W11 | Apr 1 | Multiple Views and Stereo | HW6 due |
| | Apr 3 | Multiple Views and Stereo | |
| W12 | Apr 8 | Multiple Views and Stereo | |
| | Apr 10 | Multiple Views and Stereo | Module Quiz |
| W13 | Apr 15 | Extended Topic | HW7 due |
| | Apr 17 | Extended Topic | |
| W14 | Apr 22 | Extended Topic | Final project due this week |
| | Apr 24 | Final Lecture / Presentations | No Final Exam |
---
## Technology Requirements
Assignments may require GPU access (e.g., Google Colab, Academic Jupyter). Students must avoid modifying starter code in ways that break auto-grading.
## Collaboration & Materials Policy
Discussion is allowed, but all submitted work (code, written content, reports) must be individual. Posting assignment solutions publicly is prohibited. Automated plagiarism detection (e.g., Turnitin) may be used.
## Generative AI Policy
* **Homework & Projects:** Allowed with limitations; students must understand all algorithms used.
* **Quizzes:** May be used for explanation but not direct answering.
* **Reports:** AI-assisted writing permitted with responsibility for correctness.
More detailed rules are on Canvas.
## University Policies
### Recording Policy
Classroom activities and materials may not be recorded or distributed without explicit authorization.
### COVID-19 Guidelines
Students with symptoms must contact Student Health for testing. Masking policies may change depending on conditions.
---
If you'd like, I can also produce:
* a **cleaned, typographically polished** version
* a **PDF** or **GitHub-ready README.md**
* a version styled identically to your department’s standard syllabus format

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# Math 429 Linear Algebra
# Math 429 Linear Algebra
## Course Overview
Welcome to Linear Algebra (Math 429). This course is intended as a proof based introduction to linear algebra. Topics will include: vector spaces, linear maps, eigenvalues, eigenvectors, inner product spaces, and Jordan normal form. Math 310 (or equivalent) is a prerequisite to the course, and a familiarity with proofs and proof techniques will be assumed. The course does not require but assumes a basic familiarity with the contents of Math 309 (Matrix Algebra).
Textbook: Linear Algebra Done Right 4th edition
Instructor: Jay Yang
Office Hours: Tuesday 1-2pm, Wednesday 12-1, (or by appointment)
Office: Cupples I Room 109
Email: jayy@wustl.edu
## Grading
### Homework
Homework will released every Monday, and be due every Wednesday at 11:59pm starting on September 4th. They will in general cover the week's material (Monday-Friday starting with the day the homework is released). All homework is to be submitted via Gradescope. No late homework will be accepted. Your lowest homework score will be dropped. There will be 11 homework assignments.
You may and are encouraged to discuss the homework with your classmates, but the solutions you provide must be your own work. In particular, you are expected to abide by the university policy on academic integrityLinks to an external site..
### Exams
There will be two midterm exams, held in class on Friday February 23th and Friday April 5st. The final exam will be on Tuesday May 7 10:30AM - 12:30PM. No calculators will be permitted on any of the exams. The final exam will be comprehensive.
### Final Grades
The grade will be based on your scores on the homework and exams, split as follows.
|Grade| Weights|
|---|-----|
|Homework| 40%|
|Midterms |15% each|
|Final |30%|
Your letter grade will be based off the following scale.
Letter Grades
|A |A-| B+| B| B-| C+| C| C-| D|
|---|--|--|---|---|---|---|---|---|
|90 |85 |80 |75| 70| 65| 60| 55| 50|
Schedule
This is approximate, and will be updated as we go
|Week|Sections|
|---|---|
|Week 1| 1A-1C|
|Week 2 |1C-2B|
|Week 3 |2B-3A|
|Week 4 |3B-3C|
|Week 5 |3C-3E|
|Week 6 |3E-3F|
|Week 7 |3F,5A-5B|
|Week 8 |5B-5D|
|Spring Break| |
|Week 9 |5D,5E,6A|
|Week 10|6A-6C|
|Week 11|6C|
|Week 12|7A-7B|
|Week 13|7C,8A,8B|
|Week 14|8B-8D|
## Resources
LaTeX is a way to type up your assignment. If you decide to type up your assignments, I recommend you use LaTeX. There is a bit of a learning curve though. You may for example consider using OverleafLinks to an external site., or one of many other Tex editors including TeXstudioLinks to an external site.. If you use one of these to typeset your assignment, please submit your homework as a pdf. (Don't submit the .tex files)
The textbook is available open access at [Linear Algebra Done Right](https://link.springer.com/book/10.1007/978-3-031-41026-0) to an external site.. I personally suggest having a physical copy if possible.
If you want other linear algebra textbooks, Hoffmann and Kunze's Linear Algebra is a suggestion.