Schedule and Syllabus

Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm in the NVIDIA Auditorium in the Huang Engineering Center. (map)

Discussion sections will (generally) be Fridays 12:30pm to 1:20pm in Gates B03. (map) Check Piazza for any exceptions.

This is the syllabus for the Spring 2019 iteration of the course. The syllabus for the Spring 2018, Spring 2017, Winter 2016 and Winter 2015 iterations of this course are still available.

Event TypeDateDescriptionCourse Materials
Lecture 1 Tuesday
April 2
Course Introduction
Computer vision overview
Historical context
Course logistics
Lecture 2 Thursday
April 4
Image Classification
The data-driven approach
K-nearest neighbor
Linear classification I
[python/numpy tutorial]
[image classification notes]
[linear classification notes]
Discussion Section Friday
April 5
Python / numpy / Google Cloud [notebook]
Lecture 3 Tuesday
April 9
Loss Functions and Optimization
Linear classification II
Higher-level representations, image features
Optimization, stochastic gradient descent
[linear classification notes]
[optimization notes]
Lecture 4 Thursday
April 11
Introduction to Neural Networks
Multi-layer Perceptrons
The neural viewpoint
[backprop notes]
[linear backprop example]
[derivatives notes] (optional)
[Efficient BackProp] (optional)
related: [1], [2], [3] (optional)
Discussion Section Friday
April 12
Guidelines for Picking a Project [slides]
Lecture 5 Tuesday
April 16
Convolutional Neural Networks
Convolution and pooling
ConvNets outside vision
ConvNet notes
A1 Due Wednesday
April 17
Assignment #1 due
kNN, SVM, SoftMax, two-layer network
[Assignment #1]
Lecture 6 Thursday
April 18
Deep Learning Hardware and Software
PyTorch, TensorFlow
Dynamic vs Static computation graphs
Discussion Section Friday
April 19
Intro to Pytorch and Tensorflow
12:30-13:50 at Thornton 102
[PyTorch notebook]
[TensorFlow notebook]
[gradio slides] [gradio notebook]
Lecture 7 Tuesday
April 23
Training Neural Networks, part I
Neural Nets notes 1
Neural Nets notes 2
Neural Nets notes 3
tips/tricks: [1], [2], [3] (optional)
Deep Learning [Nature] (optional)
Proposal due Wednesday
April 24
Project Proposal due [proposal description]
Lecture 8 Thursday
April 25
Training Neural Networks, part II
Update rules, ensembles, data augmentation, transfer learning
Neural Nets notes 3
Discussion Section Friday
April 26
Lecture 9 Tuesday
April 30
CNN Architectures
AlexNet, VGG, GoogLeNet, ResNet, etc
AlexNet, VGGNet, GoogLeNet, ResNet
A2 Due Wednesday
May 1
Assignment #2 due
Neural networks, ConvNets
[Assignment #2]
Lecture 10 Thursday
May 2
Recurrent Neural Networks
Language modeling
Image captioning, visual question answering
Soft attention
DL book RNN chapter (optional)
min-char-rnn, char-rnn, neuraltalk2
Discussion Section Friday
May 3
Midterm Review
Midterm Tuesday
May 7
In-class midterm
Location: TBA
Lecture 11 Thursday
May 9
Generative Models
Lecture 12 Tuesday
May 14
Detection and Segmentation
Milestone Wednesday
May 15
Project Milestone due
Lecture 13 Thursday
May 16
Visualizing and Understanding
Feature visualization and inversion
Adversarial examples
DeepDream and style transfer
Lecture 14 Tuesday
May 21
Deep Reinforcement Learning
Policy gradients, hard attention
Q-Learning, Actor-Critic
A3 Due Wednesday
May 22
Assignment #3 due
RNNs, LSTMs, Network Visualization, Style Transfer, GANs
[Assignment #3]
Lecture 15
Guest Lecture
May 23
Fairness Accountability Transparency and Ethics in AI
With a focus on Computer Vision
Timnit Gebru

Discussion Section Friday
May 24
Midterm Q&A
Lecture 16
Guest Lecture
May 28
Neuroscience and AI
Nick Haber

Lecture 17 Thursday
May 30
Human-Centered AI [slides]
Final Project Due Tuesday
June 4
Project Report due
Poster Session Tuesday
June 11
Arrillaga Alumni Center
12:00 pm to 3:30 pm