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 Type | Date | Description | Course Materials |
|---|---|---|---|
| Lecture 1 | Tuesday April 2 |
Course Introduction Computer vision overview Historical context Course logistics |
[slides] |
| Lecture 2 | Thursday April 4 |
Image Classification The data-driven approach K-nearest neighbor Linear classification I |
[slides]
[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 |
[slides]
[linear classification notes] [optimization notes] |
| Lecture 4 | Thursday April 11 |
Introduction to Neural Networks Backpropagation Multi-layer Perceptrons The neural viewpoint |
[slides]
[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 History Convolution and pooling ConvNets outside vision |
[slides]
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 CPUs, GPUs, TPUs PyTorch, TensorFlow Dynamic vs Static computation graphs |
[slides]
|
| 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 |
[slides]
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 |
[slides]
Neural Nets notes 3 |
| Discussion Section | Friday April 26 |
Backpropagation | |
| Lecture 9 | Tuesday April 30 |
CNN Architectures AlexNet, VGG, GoogLeNet, ResNet, etc |
[slides]
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 RNN, LSTM, GRU Language modeling Image captioning, visual question answering Soft attention |
[slides]
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 |
[slides]
|
| Lecture 12 | Tuesday May 14 |
Detection and Segmentation |
[slides]
|
| 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 |
[slides]
DeepDream neural-style fast-neural-style |
| Lecture 14 | Tuesday May 21 |
Deep Reinforcement Learning Policy gradients, hard attention Q-Learning, Actor-Critic |
[slides]
|
| A3 Due | Wednesday May 22 |
Assignment #3 due RNNs, LSTMs, Network Visualization, Style Transfer, GANs |
[Assignment #3] |
| Lecture 15 Guest Lecture |
Thursday 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 |
Tuesday 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 |