CPT_S-580-yan.yan1-2025-09-28-10-55-44
Title of Course: Computer Vision
Prefix and Number: CPT_S 580
Semester and Year: Fall 2025
Number of Credit Hours: 3
Prerequisites: None
Course Details
Day and Time: Tue/Thu, 1:30 - 2:45PM
Meeting Location: WILS 5
Instructor Contact Information
Instructor Name: Yan Yan
Instructor Contact Information: EME 123, yan.yan1@wsu.edu
Instructor Office Hours: Wednesday, 11:30AM - 12:30PM
Course Description
Introduction to computer vision covering classical image analysis, fundamental machine learning, and modern deep neural networks, with applications to object detection, segmentation, video understanding and generative models.
Course Materials
Required Books:
- Szeliski, R., Computer Vision: Algorithms and Applications (Springer, 2022). Online
- Mohri, M., Rostamizadeh, A., Talwalkar, A. Foundations of Machine Learning (MIT Press, 2018). Online
- Goodfellow, I., Bengio, Y., Courville, A. Deep Learning (MIT Press, 2016). Online
- Zhang, A., Lipton, Z., Li, M., Smola, A. Dive into Deep Learning. Online
Other Materials: Lecture slides (access via this Shared Folder on Google Drive)
Fees: none
|
Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
| Explain fundamental computer vision (CV) techniques and machine learning (ML) methods | Lectures, readings, discussions | Assignments (HW1, HW2), mid-term exam |
| Implement CV models for different tasks using modern deep learning platforms | Hands-on coding assignments | Assignments (HW3, HW4, HW5, HW6) |
| Analyze and design computer vision systems by evaluating deep learning architectures and developing solutions for real-world tasks |
Course project (proposal, reports, presentations) |
Project proposal, mid-term report, final report, mid-term presentation, final presentation |
| Dates | Lesson Topic | Assignment | Assessment | Course Project |
|---|---|---|---|---|
|
Week 1 |
Syllabus, Course Information, Basic CV techniques (filtering, features) | HW1: Basic CV, assigned | ||
| Week 2 08/25-08/29 |
Basic CV techniques (corner detection, descriptors) | |||
| Week 3 09/01-09/05 |
Basic ML methods |
HW1: Basic CV, due HW2: Basic ML, assigned |
||
| Week 4 09/08-09/12 |
Basic ML methods | |||
| Week 5 09/15-09/19 |
Basic neural networks (NNs) |
HW2: Basic ML, due HW3: Basic NN, assigned |
||
| Week 6 09/22-09/26 |
Basic neural networks (NNs) | Project proposal, due | ||
| Week 7 09/29-10/03 |
Convolutional neural networks (CNNs) |
HW3: Basic NN, due HW4: CNN, assigned |
||
| Week 8 10/06-10/10 |
Convolutional neural networks (CNNs), Modern NNs | Mid-term quiz (Basic CV techniques, Basic ML methods and Basic NNs) | ||
| Week 9 10/13-10/17 |
Modern NNs, Object detection and segmentation |
HW4: CNN, due HW5: Object detection, assigned |
||
| Week 10 10/20-10/24 |
Object detection and segmentation |
|
Project mid-term report, due | |
| Week 11 10/27-10/31 |
Object detection and segmentation, Mid-term presentation |
HW5: Object detection, due HW6: Image segmentation, assigned |
Mid-term presentation | |
| Week 12 11/03-11/07 |
Video Classification |
|
||
| Week 13 11/10-11/14 |
Image and video generation | HW6: Image segmentation, assigned | ||
| Week 14 11/17-11/21 |
Multimodality and foundation models | Project final report, due | ||
| Week 15 12/01-12/05 |
Final project presentation | Final project presentation |
Expectations for Student Effort
For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class.
Grading
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
| Assignments (6 HW total) | 100 points each assignment | 60% |
| Mid-term quiz | 100 points | 5% |
| Course project proposal | 100 points | 4% |
| Course project mid-term report | 100 points | 8% |
| Course project final report | 100 points | 8% |
| Course project mid-term presentation | 100 points | 5% |
| Course project final presentation | 100 points | 5% |
| Attendance | 100 points | 5% |
Final Exam: This course does not include a final exam. The final project serves as the culminating assessment for the course.
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
94-100 |
C | 70-73 |
| A- | 90-93 | C- | 66-69 |
| B+ | 86-89 | D+ | 62-65 |
| B | 82-85 | D | 58-61 |
| B- | 78-81 | F | 0-57 |
| C+ | 74-77 |
Grades will be rounded to the nearest tenth (e.g., 89.5% = A-)
Attendance and Make-Up Policy
Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.
The mid-term quiz will be administered via Canvas as an online quiz. Students are expected to complete the exam during the scheduled time window. Make-up exams will only be offered in documented cases of illness, family emergency, or university-excused absence. Students must notify the instructor as soon as possible, preferably before the exam window closes, and provide appropriate documentation. Without prior approval and documentation, missed exams will result in a score of zero.
Late homework and make-up will not be accepted unless pre-approved with documentation.
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-Receive a failing grade for the assignment or exam
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.
Graduate Student Rights and Responsibilities
This is a graduate-level course. Graduate students are expected to be familiar with and abide by the Graduate School’s policies on academic integrity, student rights, and responsibilities. Details can be found on the Graduate School’s website at this link.
AI Statement
Generative Artificial Intelligence (AI) tools (e.g., ChatGPT, Copilot, Bard) may be used in this course under limited conditions. The use of generative AI is encouraged for general learning support, such as searching for background information or clarifying concepts (e.g., “What is the architecture of ResNet?”). However, generative AI must not be used to generate code or solutions for any graded assignments, projects or exams. Doing so will be considered a violation of WSU’s Academic Integrity Policy.
Generative AI is strictly prohibited during the mid-term quiz. Students are expected to complete all graded work independently, except where explicit collaboration is permitted by the instructor.
Any acceptable use of generative AI must follow the same standards of academic honesty as other resources: students are responsible for providing proper attribution when ideas or materials are derived from AI outputs. If in doubt, ask the instructor before using such tools.
University Syllabus Statement
This course syllabus includes course-specific policies and information. In addition, all Washington State University courses are governed by the University Syllabus, which provides important policies, resources, and student rights information. Students are responsible for reading and understanding the University Syllabus, available at https://syllabus.wsu.edu/university-syllabus/.