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.

DateDescriptionCourse MaterialsEventsDeadlines
03/29 Lecture 1: Introduction
Computer vision overview
Historical context
Course logistics
[slides 1] [slides 2]
——— Deep Learning Basics
03/31 Lecture 2: Image Classification with Linear Classifiers
The data-driven approach
K-nearest neighbor
Linear Classifiers
Algebraic / Visual / Geometric viewpoints
SVM and Softmax loss
Image Classification Problem
Linear Classification
04/01 Python / Numpy Review Session
[Colab] [Tutorial]
1:30-2:30pm PT Assignment 1 out
[handout] [colab]
04/05 Lecture 3: Regularization and Optimization
Stochastic Gradient Descent
Momentum, AdaGrad, Adam
Learning rate schedules
04/07 Lecture 4: Neural Networks and Backpropagation
Multi-layer Perceptron
Linear backprop example
Suggested Readings:
  1. Why Momentum Really Works
  2. Derivatives notes
  3. Efficient backprop
  4. More backprop references: [1], [2], [3]
04/08 Backprop Review Session
1:30-2:30pm PT
——— Perceiving and Understanding the Visual World
04/12 Lecture 5: Image Classification with CNNs
Higher-level representations, image features
Convolution and pooling
Convolutional Networks
04/13 Final Project Overview and Guidelines
3:00-4:00pm PT
04/14 Lecture 6: CNN Architectures
Batch Normalization
Transfer learning
AlexNet, VGG, GoogLeNet, ResNet
AlexNet, VGGNet, GoogLeNet, ResNet
04/15 Assignment 2 out
[handout] [colab]
Assignment 1 due
04/18 Project proposal due
04/19 Lecture 7: Training Neural Networks
Activation functions
Data processing
Weight initialization
Hyperparameter tuning
Data augmentation
Neural Networks, Parts 1, 2, 3
Suggested Readings:
  1. Stochastic Gradient Descent Tricks
  2. Efficient Backprop
  3. Practical Recommendations for Gradient-based Training
  4. Deep Learning, Nature 2015
  5. An Overview of Gradient Descent Algorithms
  6. A Disciplined Approach to Neural Network Hyper-Parameters
04/21 Lecture 8: Visualizing and Understanding
Feature visualization and inversion
Adversarial examples
DeepDream and style transfer
04/22 PyTorch Review Session
1:30-2:30pm PT
04/26 Lecture 9: Object Detection and Image Segmentation
Single-stage detectors
Two-stage detectors
Semantic/Instance/Panoptic segmentation
FCN, R-CNN, Fast R-CNN, Faster R-CNN, YOLO
04/28 Lecture 10: Recurrent Neural Networks
Language modeling
Image captioning
Suggested Readings:
  1. DL book RNN chapter
  2. Understanding LSTM Networks
04/29 Object Detection & RNNs Review Session
2:30-3:30pm PT
05/02 Assignment 2 due
05/03 Lecture 11: Attention and Transformers
Suggested Readings:
  1. Attention is All You Need [Original Transformers Paper]
  2. Attention? Attention [Blog by Lilian Weng]
  3. The Illustrated Transformer [Blog by Jay Alammar]
  4. ViT: Transformers for Image Recognition [Paper] [Blog] [Video]
  5. DETR: End-to-End Object Detection with Transformers [Paper] [Blog] [Video]
05/5 Lecture 12: Video Understanding
Video classification
Two-stream networks
Multimodal video understanding
05/06 Midterm Review Session
2:30-3:30pm PT
05/07 Project milestone due
05/10 In-Class Midterm
1:30-3:00pm Assignment 3 out [handout] [colab]
——— Reconstructing and Interacting with the Visual World
05/12 Lecture 13: Generative Models
Supervised vs. Unsupervised learning
Pixel RNN, Pixel CNN
Variational Autoencoders
Generative Adversarial Networks
Suggested Readings:
  1. Image GPT: Generative Pretraining From Pixels [Paper] [Blog]
05/17 Lecture 14: Self-supervised Learning
Pretext tasks
Contrastive learning
Multisensory supervision
Suggested Readings:
  1. Lilian Weng Blog Post
  2. DINO: Emerging Properties in Self-Supervised Vision Transformers [Paper] [Blog] [Video]
05/19 Lecture 15: Low-Level Vision
(Guest Lecture by Prof. Jia Deng from Princeton University)
Optical flow
Depth estimation
Stereo vision
05/24 Lecture 16: 3D Vision
3D shape representations
Shape reconstruction
Neural implicit representations
Assignment 3 due
——— Human-Centered Applications and Implications
05/26 Lecture 17: Human-Centered Artificial Intelligence
AI & healthcare
05/31 Lecture 18: Fairness in Visual Recognition
(Guest Lecture by Prof. Olga Russakovsky from Princeton University)
06/02 Project final report due
06/04 Final Project Poster Session Note: Only open to the Stanford community and invited guests.


Location: Alumni Center McCaw Hall/Ford Gardens

Click here for the logistics and expectations.
06/05 Project poster PDF due