EECS 442 is an introductory computer vision
class. Class topics include low-level vision,
object recognition, motion, 3D reconstruction,
basic signal processing, and deep
learning. We'll also touch on very recent
advances, including image synthesis,
self-supervised learning, and embodied
Lectures will take place Monday and Wednesday, 3:00 - 4:30pm. Attendance will not be required, but it is highly encouraged. There are multiple ways to participate:
- In person in Stamps Auditorium.
- Live-streamed on Zoom. Please see here for the link. Due to the challenges of holding a hybrid lecture, we will prioritize the in-person experience. We cannot guarantee that we will always answer the questions of Zoom attendees (those messages are unfortunately easy to miss!). In the event of technical problems, we will close the Zoom sesion, and direct attendees to the lecture recording.
- We'll post lecture recordings online here.
This class has three discussion sections. Please note that two of the sections listed on the course schedule will not be offered (namely, the Friday 10:30am and Thursday 3:30pm sections). You may attend any section, and attendance is not required. We'll post video recordings of one section, for those who are unable to attend.
|Thu 4:30-5:30pm||1303 EECS|
|Fri 12:30-1:30pm||220 Chrysler (new location)|
|Fri 1:30-2:30pm||1500 EECS (new location)|
- This course puts a strong emphasis on mathematical methods. We'll cover a wide range of techniques in a short amount of time. Background in linear algebra is required. For a refresher, please see here. This material should mostly look familiar to you.
- This class will require a significant amount of programming. All programming will be completed in Python, using numerical libraries such as numpy, scipy, and PyTorch. In some assignments, we'll give you starter code; in others, we'll ask you to write a large amount of code from scratch.
Google Colab: The problem sets will be completed using Jupyter notebooks, generally using Google Colab. While this service is free, it is important to note that it comes with GPU usage limits. You may only use the GPUs on a given Google account for a certain number of hours per day. These limits are due to the fact that GPUs are very expensive. Since none of the problem sets require training large models, you may never encounter these limits. However, we have provided a few suggestions for avoiding them:
- Reduce your GPU usage by debugging your code on the CPU. For example, after confirming that you can successfully complete a single training iteration without an error on the CPU, you can switch to the GPU. You can then switch back to the CPU if you need to debug further errors.
- Since many of the machines in the CAEN computer labs have NVIDIA GPUs with 4GB or more of RAM, you can connect to them remotely and train deep learning models. We have provided instructions for using these GPUs here. In our experience, this approach is also less reliable, so we recommend using Colab when possible. Also, since the problem sets are designed for Colab, running them here will require minor modifications, which we’ve described in the tutorial.
- We note that the limit is per account (e.g., UMich email or Gmail account).
- Consider purchasing Google Colab Pro ($10/month) during the portion of the class where GPUs are required (PS5 and onward; approximately 2 months). For students who would like to use this (optional) service, but are unable to afford it, we have been provided with a small amount of funding from the CSE DEI office. Please send the course staff a private message over Piazza if you would like to learn more about this option.
Q&A: This course has a Piazza forum, where you can ask public questions. If you cannot make your post public (e.g., due to revealing problem set solutions), please mark your post private, or come to office hours. Please note, however, that the course staff cannot provide help debugging code, and there is no guarantee that they'll be able to answer all questions — especially last-minute questions about the homework. We also greadly appreciate it when you respond to questions from other students! If you have an important question that you would prefer to discuss over email, you may email the course staff (firstname.lastname@example.org), or you can contact the instructor by email directly.
Homework: There will be homework assignments approximately every week. All programming assignments are to be completed in Python, using the starter code that we provide. Assignments will always be due at midnight (11:59pm) on the due date. The assignments will all be worth approximately equal amounts. Written problems will usually be submitted to Gradescope. You may be asked to annotate your pdf (e.g. by selecting your solution to each problem).
Final project: In lieu of a final exam, we'll have final project. This project will be completed in small groups during the last weeks of the class. The direction for this project is open-ended: you can either choose from a list of project ideas that we distribute, or you can propose a topic of your own. A short project proposal will be due approximately halfway through the course. During the final exam period, you'll turn in a final report and give a short presentation. You may use an ongoing research work for your final project, as long it meets the requirements.
Textbook: There are no required textbooks to purchase. We'll be using draft versions of two books:
- Torralba, Isola, Freeman. Foundations of Computer Vision Draft manuscript chapters provided in class.
- Szeliski. Computer Vision: Algorithms and Applications, 2nd edition draft (available for free online)
The following textbooks may also be useful as references:
- Goodfellow, Bengio, Courville. Deep Learning. (available for free online)
- Hartley and Zisserman. Multiple View Geometry in Computer Vision.
- Forsyth and Ponce. Computer Vision: A Modern Approach.
Acknowledgements: This course uses material from MIT's 6.869: Advances in Computer Vision and its associated textbook manuscript, Foundations of Computer Vision, by Antonio Torralba, Phillip Isola, and William Freeman. It also includes lecture slides from other researchers, including Svetlana Lazebnik, Alexei Efros, David Fouhey, and Noah Snavely (please see acknowledgments in the lecture slides).
Late policy: You'll have 5 late days to use over the course of the semester. Each time you use one, you may submit a homework assignment one day late without penalty. You are allowed to use multiple late days on a single assignment. For example, you can use all of your days at once to turn in one assignment a week late. You do not need to notify us when you use a late day; we'll deduct it automatically. If you run out of late days and still submit late, your assignment will be penalized at a rate of 1% per hour. If you edit your assignment after the deadline, this will count as a late submission, and we'll use the revision time as the date of submission (after a short grace period of a few minutes). We will not provide additional late time, except under exceptional circumstances, and for these we'll require documentation (e.g., a doctor's note). Please note that the late days are provided to help you deal with minor setbacks, such as routine illness or injury, paper deadlines, interviews, and computer problems; these do not generally qualify for an additional extension.
Please note that, due to the number of late days available, there will be a long (2+ week) lag between the time of submission and the time that grades are released. We'll need to wait for the late submissions to arrive before we can complete the grading.
Regrade requests: If you think there was a grading error, you'll have 9 days to submit a regrade request, using Gradescope. This will be a strict deadline, even for significant mistakes such as missing grades, so please look over your graded assignments!
Support: The counseling and psychological services center (CAPS) provides support for a variety of issues, including for mental health and stress.
- Grades will be computed as follows, with all homeworks equally weighted:
Homework 70% Final project 30%
- We'll use these approximate grade thresholds:
A+ Curved A 92% A- 90% B+ 88% B 82% B- 80% C+ 78% C 72% C- 70%
Academic integrity: While you are encouraged to discuss homework assignments with other students, your programming work must be completed individually. You must also write up your solution on your own. You may not search for solutions online, or to use existing implementations of the algorithms in the assignments. Please see the Michigan engineering honor code for more information.