# Stanford University CS231n: Deep Learning for Computer Vision

https://cs231n.stanford.edu/schedule.html

[CS231n Home](https://cs231n.stanford.edu/index.html)
  * [**Schedule**](https://cs231n.stanford.edu/schedule.html)
  * [**Assignments**](https://cs231n.stanford.edu/assignments.html)
  * [**Project**](https://cs231n.stanford.edu/project.html)
  * [**Office Hours**](https://cs231n.stanford.edu/office_hours.html)
  * [**Lecture Videos**](https://canvas.stanford.edu/courses/222471/external_tools/69960)
  * [**Ed**](https://edstem.org/us/courses/97362/discussion)
  * [**Useful Notes**](https://cs231n.github.io/)


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# CS231n: Deep Learning for Computer Vision
### Stanford - Spring 2026
## Schedule
  * **Lectures** will occur Tuesdays and Thursdays from 12:00-1:20pm Pacific Time at [NVIDIA Auditorium](https://goo.gl/maps/hRjQYd6MqxB2).
  * **Discussion** sections will (generally) occur on Fridays from 12:30-1:20pm Pacific Time at [NVIDIA Auditorium](https://goo.gl/maps/hRjQYd6MqxB2). Check [Ed](https://edstem.org/us/courses/97362/discussion) for any exceptions.

Updated lecture slides will be posted here shortly before each lecture. For ease of reading, we have color-coded the lecture category titles in blue, discussion sections (and final project poster session) in yellow, and the midterm exam in red. Note that the schedule is subject to change as the quarter progresses.   
| Date  | Description  | Course Materials  | Events  | Deadlines  |  
| --- | --- | --- | --- | --- |  
| Mar 31  |  **Lecture 1: Introduction**   
Computer vision overview   
Course overview   
Course logistics   
[[slides 1](https://cs231n.stanford.edu/slides/2026/lecture_1_part_1.pdf)] [[slides 2](https://cs231n.stanford.edu/slides/2026/lecture_1_part_2.pdf)]   |   |   |   |  
| ———  |  **Deep Learning Basics**  |   |   |   |  
| Apr 02  |  **Lecture 2: Image Classification with Linear Classifiers**   
The data-driven approach  
K-nearest neighbor  
Linear Classifiers  
Algebraic / Visual / Geometric viewpoints  
Softmax loss  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_2.pdf)]   |  [Image Classification Problem](https://cs231n.github.io/classification/)  
[Linear Classification](https://cs231n.github.io/linear-classify/)  
 |  Assignment 1 ** out **  |   |  
| Apr 03  |  Python / Numpy Review Session   
[[Colab](https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb)] [[Tutorial](https://cs231n.github.io/python-numpy-tutorial/)]   |  12:30-1:20pm PT   |   |   |  
| Apr 07  |  **Lecture 3: Regularization and Optimization**   
Regularization   
Stochastic Gradient Descent  
Momentum, AdaGrad, Adam  
Learning rate schedules  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_3.pdf)]   |  [Optimization](https://cs231n.github.io/optimization-1/)  
 |   |   |  
| Apr 09  |  **Lecture 4: Neural Networks and Backpropagation**   
Multi-layer Perceptron  
Backpropagation  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_4.pdf)]   |  [Backprop](http://cs231n.github.io/optimization-2)  
[Linear backprop example](https://cs231n.stanford.edu/handouts/linear-backprop.pdf)  
Suggested Readings: 
  1. [Why Momentum Really Works](https://distill.pub/2017/momentum/)
  2. [Derivatives notes](https://cs231n.stanford.edu/handouts/derivatives.pdf)
  3. [Efficient backprop](https://cs231n.stanford.edu/papers/lecun-98b.pdf)
  4. More backprop references: [[1]](http://colah.github.io/posts/2015-08-Backprop/), [[2]](http://neuralnetworksanddeeplearning.com/chap2.html), [[3]](https://www.youtube.com/watch?v=q0pm3BrIUFo)

 |   |   |  
| Apr 10  |  Backprop Review Session   
[[Colab](https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/backprop.ipynb)] [[slides](https://cs231n.stanford.edu/slides/2026/section_2_backprop.pdf)]   |  12:30-1:20pm PT   |   |   |  
| ———  |  **Perceiving and Understanding the Visual World**  |   |   |   |  
| Apr 14  |  **Lecture 5: Image Classification with CNNs**   
History  
Higher-level representations, image features  
Convolution and pooling  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_5.pdf)]   |  [Convolutional Networks](http://cs231n.github.io/convolutional-networks)  
 |   |   |  
| Apr 16  |  **Lecture 6: CNN Architectures**   
Batch Normalization  
Transfer learning  
AlexNet, VGG, ResNet  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_6.pdf)]   |  [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf), [VGGNet](https://arxiv.org/abs/1409.1556), [GoogLeNet](https://arxiv.org/abs/1409.4842), [ResNet](https://arxiv.org/abs/1512.03385)  |  Project Proposal ** out **  
 |  Assignment 1 ** due **  
 |  
| Apr 17  |  Final Project Overview and Guidelines   
[[slides](https://cs231n.stanford.edu/slides/2026/section_3_project.pdf)]   |  12:30-1:20pm PT   |   |   |  
| Apr 21  |  **Lecture 7: Recurrent Neural Networks**   
RNN, LSTM, GRU  
Language modeling  
Image captioning  
Sequence-to-sequence  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_7.pdf)]   |  Suggested Readings: 
  1. [DL book RNN chapter](http://www.deeplearningbook.org/contents/rnn.html)  

  2. [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)  


 |   |   |  
| Apr 23  |  **Lecture 8: Attention and Transformers**   
Self-Attention   
Transformers   
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_8.pdf)]   |  Suggested Readings: 
  1. Attention is All You Need [[Original Transformers Paper](https://arxiv.org/abs/1706.03762)]   

  2. Attention? Attention [[Blog by Lilian Weng](https://lilianweng.github.io/posts/2018-06-24-attention/)]   

  3. The Illustrated Transformer [[Blog by Jay Alammar](http://jalammar.github.io/illustrated-transformer/)]   

  4. ViT: Transformers for Image Recognition [[Paper](https://arxiv.org/abs/2010.11929)] [[Blog](https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html?m=1)] [[Video](https://www.youtube.com/watch?v=TrdevFK_am4)]   

  

 |  Assignment 2 ** out **  
 |  Project Proposal ** due **  
 |  
| Apr 24  |  PyTorch Review Session   
[[Colab](https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/pytorch.ipynb)]   |  12:30-1:20pm PT   |   |   |  
| Apr 28  |  **Lecture 9: Object Detection, Image Segmentation, Visualizing and Understanding**   
Single-stage detectors  
Two-stage detectors  
Semantic/Instance/Panoptic segmentation  
Feature visualization and inversion   
Adversarial examples   
DeepDream and style transfer   
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_9.pdf)]   | 
  1. [FCN](https://arxiv.org/abs/1411.4038), [R-CNN](https://arxiv.org/abs/1311.2524), [Fast R-CNN](https://arxiv.org/abs/1504.08083), [Faster R-CNN](https://arxiv.org/abs/1506.01497), [YOLO](https://arxiv.org/abs/1506.02640)
  2. DETR: End-to-End Object Detection with Transformers [[Paper](https://arxiv.org/abs/2005.12872)] [[Blog](https://ai.facebook.com/blog/end-to-end-object-detection-with-transformers/)] [[Video](https://www.youtube.com/watch?v=utxbUlo9CyY)]

 |   |   |  
| Apr 30  |  **Lecture 10: Video Understanding**   
Video classification  
3D CNNs  
Two-stream networks  
Multimodal video understanding  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_10.pdf)]   |   |   |  
| May 01  |  RNNs & Transformers   
[[slides](https://cs231n.stanford.edu/slides/2026/section_5.pdf)]   |  12:30-1:20pm PT   |   |   |  
| May 05  |  **Lecture 11: Large Scale Distributed Training**   
Utilization, Parallelism, and Activation Checkpointing  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_11.pdf)]   |   |   |  
| ———  |  **Generative and Interactive Visual Intelligence**  |   |   |   |  
| May 07  |  **Lecture 12: Self-supervised Learning**   
Pretext tasks  
Contrastive learning  
Multisensory supervision  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_12.pdf)]   |  Suggested Readings: 
  1. [Lilian Weng Blog Post](https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html)  

  2. DINO: Emerging Properties in Self-Supervised Vision Transformers [[Paper](https://arxiv.org/abs/2104.14294)] [[Blog](https://ai.facebook.com/blog/dino-paws-computer-vision-with-self-supervised-transformers-and-10x-more-efficient-training)] [[Video](https://youtu.be/h3ij3F3cPIk)]   


 |   |   |  
| May 08  |  Midterm Review Session   
 |  12:30-1:20pm PT   |   |  Assignment 2 ** due **  
 |  
| May 12  |  **In-Class Midterm**   
 |  12:00-1:20pm PT   |   |   |  
| May 14  |  **Lecture 13: Generative Models 1**   
Variational Autoencoders   
Generative Adversarial Network   
Autoregressive Models   
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_13.pdf)]   |  Suggested Readings: 
  1. [Blog: ELBO — What & Why](https://yunfanj.com/blog/2021/01/11/ELBO.html)  


 |  Assignment 3 ** out **  
 |   |  
| May 15  |   |   |   |  Milestone 1 Check-In ** due **  |  
| May 19  |  **Lecture 14: Generative Models 2**   
Diffusion models   
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_14.pdf)]   |   |   |   |  
| May 21  |  **Lecture 15: 3D Vision**   
3D shape representations  
Shape reconstruction  
Neural implicit representations  
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_15.pdf)]   |   |   |   |  
| May 22  |   |   |   |  Milestone 2 Check-In ** due **  |  
| May 26  |  **Lecture 16: Vision and Language**   
[[slides](https://cs231n.stanford.edu/slides/2026/lecture_16.pdf)]   |   |   |   |  
| May 28  |  **Lecture 17: World Modeling**   
Guest Lecturer: Prof. Gordon Wetzstein  
 |   |   |  Assignment 3 ** due **  
 |  
| May 29  |   |   |   |  Milestone 3 Check-In ** due **  |  
| Jun 02  |  **Lecture 18: Human-Centered AI**  
 |   |   |   |  
| Jun 05  |   |   |   |  Final Report ** due **  |  
| Jun 10  |  **Final Project Poster Session**   
 |  12:15-3:15 PM PT   
Location: Gates Patio + HAI   |   |   |