Schedule

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.

DateDescriptionLecturerCourse MaterialsEventsDeadlines
04/01 Lecture 1: Introduction
Computer vision overview
Course overview
Course logistics
[slides 1] [slides 2] 		Fei-Fei Li, Ehsan Adeli
——— Deep Learning Basics
04/03 Lecture 2: Image Classification with Linear Classifiers
The data-driven approach
K-nearest neighbor
Linear Classifiers
Algebraic / Visual / Geometric viewpoints
Softmax loss
[slides] 		Ehsan Adeli Image Classification Problem
Linear Classification
04/04 Python / Numpy Review Session
[Colab] [Tutorial] 		12:30-1:20pm PT
04/08 Lecture 3: Regularization and Optimization
Regularization
Stochastic Gradient Descent
Momentum, AdaGrad, Adam
Learning rate schedules
[slides] 		Zane Durante Optimization
04/09 Assignment 1 out
04/10 Lecture 4: Neural Networks and Backpropagation
Multi-layer Perceptron
Backpropagation
[slides] 		Ehsan Adeli Backprop
Linear backprop example
Suggested Readings:
  1. Why Momentum Really Works
  2. Derivatives notes
  3. Efficient backprop
  4. More backprop references: [1], [2], [3]
04/11 Backprop Review Session
[slides] [Colab] 		12:30-1:20pm PT
——— Perceiving and Understanding the Visual World
04/15 Lecture 5: Image Classification with CNNs
History
Higher-level representations, image features
Convolution and pooling
[slides] 		Justin Johnson Convolutional Networks
04/17 Lecture 6: CNN Architectures
Batch Normalization
Transfer learning
AlexNet, VGG, ResNet
[slides] 		Zane Durante AlexNet, VGGNet, GoogLeNet, ResNet
04/18 Final Project Overview and Guidelines
[slides] 		12:30-1:20pm PT
04/22 Lecture 7: Recurrent Neural Networks
RNN, LSTM, GRU
Language modeling
Image captioning
Sequence-to-sequence
[slides] 		Zane Durante Suggested Readings:
  1. DL book RNN chapter
  2. Understanding LSTM Networks
04/23 Assignment 2 out
 Assignment 1 due
04/24 Lecture 8: Attention and Transformers
Self-Attention
Transformers
[slides] 		Justin Johnson 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]

04/25 PyTorch Review Session
12:30-1:20pm PT Project proposal due
04/29 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
Ehsan Adeli
05/01 Lecture 10: Video Understanding
Video classification
3D CNNs
Two-stream networks
Multimodal video understanding
 Ruohan Gao
05/02 RNNs & Transformers
12:30-1:20pm PT
05/06 Lecture 11: Large Scale Distributed Training
Justin Johnson
05/07 Assignment 2 due
——— Generative and Interactive Visual Intelligence
05/08 Lecture 12: Self-supervised Learning
Pretext tasks
Contrastive learning
Multisensory supervision
 Ehsan Adeli
05/09 Midterm Review Session
12:30-1:20pm PT
05/13 In-Class Midterm
12:00-1:20pm PT
05/14 Assignment 3 out
05/15 Lecture 13: Generative Models 1
Variational Autoencoders
Generative Adversarial Network
Autoregressive Models
Justin Johnson
05/16 Project milestone due
05/20 Lecture 14: Generative Models 2
Diffusion models
Justin Johnson
05/22 Lecture 15: Vision and Language
Ranjay Krishna
05/27 Lecture 16: 3D Vision
3D shape representations
Shape reconstruction
Neural implicit representations
 Jiajun Wu
05/28 Assignment 3 due
05/29 Lecture 17: Robot Learning
Deep Reinforcement Learning
Model Learning
Robotic Manipulation
 Yunzhu Li
06/03 Lecture 18: Human-Centered AI
 Fei-Fei Li or James Landay
06/04 Project report due
06/11 Final Project Poster Session