Lecture Date Topic Materials Assignments
Lec. 1 Mon, Aug. 29 Introduction
About the course
Simple edge detection
ps1 out (filtering)
Lec. 2 Wed, Aug. 31 Linear filtering
Gradient filters
Sec. 1 Fri, Sep. 2 Linear algebra and filtering
Mon, Sep. 5 No class - Labor Day
Lec. 3 Wed, Sep. 7 Image pyramids
Gaussian pyramid
Laplacian pyramid
Nonlinear filtering
Sec. 2 Fri, Sep. 9 Fourier Transform
Lec. 4 Mon, Sep. 12 Frequency
Image bases
Fourier transform
Lec. 5 Wed, Sep. 14 Machine learning
Nearest neighbor
Linear regression
ps1 due
ps2 out (frequency)
Sec. 3 Fri, Sep. 16 Pyramids and Fourier Transform
Lec. 6 Mon, Sep. 19 Linear classifiers
Logistic regression
Stochastic gradient descent
Lec. 7 Wed, Sep. 21 Neural networks
Network structure
ps2 due
ps3 out (intro to ML)
Sec. 4 Fri, Sep. 23 Machine learning tutorial
Lec. 8 Mon, Sep. 26 Optimization
Computation graphs
Lec. 9 Wed, Sep. 28 Convolutional networks
Convolution layers
ps3 due
ps4 out (backprop)
Sec. 5 Fri, Sep. 30 Backpropagation
Lec. 10 Mon, Oct. 3 Scene understanding
Semantic segmentation
Fully convolutional nets
Intro to object detection
Lec. 11 Wed, Oct. 5 Object detection
Sliding window
Region-based CNNs
Instance segmentation
ps4 due
ps5 out (scene recognition)
Sec. 6 Fri, Oct. 7 PyTorch tutorial
Lec. 12 Mon, Oct. 10 Image Synthesis with GANs
Texture synthesis
Conditional GANs
Lec. 13 Wed, Oct. 12 Recent Advances in Image Synthesis
ps5 due
ps6 out (image synthesis)
Sec. 7 Fri, Oct. 14 Office hours + GANs
Mon, Oct. 17 No class - Fall Break
Lec. 14 Wed, Oct. 19 Temporal models
3D convolutions
Recurrent networks
proposal info out
Sec. 8 Fri, Oct. 21 Object detection + project office hours
    Lec. 15 Mon, Oct. 24 Representation learning (Zoom lecture)
    Transfer learning
    Lec. 16 Wed, Oct. 26 Language (Zoom lecture)
    ps6 due
    ps7 out (object detection)
    Sec. 9 Fri, Oct. 28 Object detection + project office hours
    Lec. 17 Mon, Oct. 31 Sound and touch
    Neural nets for other signals
    Multimodal self-supervision
    Lec. 18 Wed, Nov. 2 Image formation
    Camera models
    Plenoptic function
    Sec. 10 Fri, Nov. 4 Project office hours
      Lec. 19 Mon, Nov. 7 Multi-view geometry
      Lec. 20 Wed, Nov. 9 Fitting geometric models
      ps7 due
      ps8 out (panorama stitching)
      Sec. 11 Fri, Nov. 11 Geometry + Project office hours
      Lec. 21 Mon, Nov. 14 Structure from motion
      Structure from motion
      Multi-view stereo
      Stereo algorithms
      Lec. 22 Wed, Nov. 16 Motion estimation
      Optical flow
      Aperture problem
      ps8 due
      ps9 out (representation learning)
      project guidelines
      Sec. 11 Fri, Nov. 18 Project office hours
        Lec. 23 Mon, Nov. 21 Light, shading, and color
        Shape from shading
        Intrinsic images
        Color perception
        Wed, Nov. 23 No class - Thanksgiving
        Fri, Nov. 25 No class - Thanksgiving
        Mon, Nov. 28 No class
        Lec. 24 Wed, Nov. 30 Embodied vision (Zoom lecture)
        Learning from demonstrations
        Reinforcement learning
        ps9 due
        ps10 out (EECS 504 only)
        Sec. 12 Fri, Dec. 2 Project office hours
          Lec. 25 Mon, Dec. 5 Recent advances in network architectures
          Implicit representations
          Vision transformers
          Lec. 26 Wed, Dec. 7 Ethics, bias, and disinformation
          Algorithmic fairness
          Image forensics
          ps10 due

          Staff & Office Hours

          Office Hours

          Day Time Name Location
          Monday12:00pm - 1:00pm Kshama Nitin ShahEECS 3312
          4:30pm - 5:00pm Andrew Owens EECS 4231 + Zoom
          Tuesday11am-12pm Harshal Bora Zoom
          4:00pm - 5:00pm Fengyu YangEECS 3312
          Wednesday10:00am - 11:00am Rakesh Chowdhary MachineniEECS 3312
          1:30pm - 2:30pm Jim YangZoom with queue
          Thursday 11:00am-12:00pm Harshal BoraZoom
          3:00pm-4:00pmZixuan PanEECS 3312
          Friday 1:30pm - 2:30pm Jim YangEECS 3312
          1:30pm - 2:30pm Andrew OwensEECS 4231 + Zoom

          Office hours will be offered in person and over Zoom, using the same link as lecture. When you join the call, please enter your name in the chat, so that we know the order that you joined. You will then meet one-on-one with a member of the course staff.

          To keep track of office hours times, you may find it helpful to subscribe to the class calendar.

          Course information

          EECS 442/504 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 perception.

          EECS 442 vs. 504: EECS 442 is an advanced undergraduate-level class, while EECS 504 is a graduate-level class. The two classes will have a single, shared lecture. Students enrolled in 504 will tasked with longer and more mathematically rigorous homework assignments, and will have higher expectations for the final project.

          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 220 Chrysler. Due to space limits, this is only available to students who have registered for the in-person version of the class.
          • Live-streamed on Zoom. Please see here for the link. Please do not share it, so that we can avoid Zoom bombing.
          • We'll post lecture recordings online here.

          Discussion section:
          This class has three discussion sections. One of these sections will be simultaneously streamed over Zoom, using the same Zoom link as lecture. We'll post video recordings of this section, as well.

          Time Place
          Thu 4:30-5:30pm 1010 DOW
          Fri 10:30-11:30am1200 EECS
          Fri 12:30-1:30pm G906 COOL & Zoom
          Some weeks, we'll host tutorials during these sections, where GSIs will cover a topic in depth. These tutorials appear in the schedule. Attendance to these tutorials is optional, and recordings will be posted online. Other weeks, the discussion section will function as additional office hours and project discussion.


          • 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:

          1. 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.
          2. 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.
          3. We note that the limit is per account (e.g., UMich email or Gmail account).
          4. 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 (, 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). Students enrolled in EECS 504 will have more homework problems than those in 442.

          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. Students in EECS 504 are expected to conduct a literature review of their chosen area as part of the project proposal.

          Textbook: There are no required textbooks to purchase. We'll be using draft versions of two books:

          • Isola, Torralba, Freeman. 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 draws heavily from MIT's 6.869: Advances in Computer Vision by Antonio Torralba, William Freeman, and Phillip Isola. 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 7 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 30% per day. 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%
            These are lower bounds on letter score grades. For example, if you get an 81%, you will get a B- or better. We may gently curve the class up, in a way that would only improve your letter grade: e.g., after the curve, an 81% might round up to a B, but it would not round down to a C+.

          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.