Schedule and Syllabus

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

This is the syllabus for the Spring 2017 iteration of the course. The syllabus for the Winter 2016 and Winter 2015 iterations of this course are still available.
Event TypeDateDescriptionCourse Materials
Lecture 1 Tuesday
April 4
Course Introduction
Computer vision overview
Historical context
Course logistics
Lecture 2 Thursday
April 6
Image Classification
The data-driven approach
K-nearest neighbor
Linear classification I
[python/numpy tutorial]
[image classification notes]
[linear classification notes]
Lecture 3 Tuesday
April 11
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 13
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)
Lecture 5 Tuesday
April 18
Convolutional Neural Networks
Convolution and pooling
ConvNets outside vision
ConvNet notes
Lecture 6 Thursday
April 20
Training Neural Networks, part I
Activation functions, initialization, dropout, batch normalization
Neural Nets notes 1
Neural Nets notes 2
Neural Nets notes 3
tips/tricks: [1], [2], [3] (optional)
Deep Learning [Nature] (optional)
A1 Due Thursday
April 20
Assignment #1 due
kNN, SVM, SoftMax, two-layer network
[Assignment #1]
Lecture 7 Tuesday
April 25
Training Neural Networks, part II
Update rules, ensembles, data augmentation, transfer learning
Neural Nets notes 3
Proposal due Tuesday
April 25
Couse Project Proposal due [proposal description]
Lecture 8 Thursday
April 27
Deep Learning Software
Caffe, Torch, Theano, TensorFlow, Keras, PyTorch, etc
Lecture 9 Tuesday
May 2
CNN Architectures
AlexNet, VGG, GoogLeNet, ResNet, etc
Lecture 10 Thursday
May 4
Recurrent Neural Networks
Language modeling
Image captioning, visual question answering
Soft attention
DL book RNN chapter (optional)
min-char-rnn, char-rnn, neuraltalk2
A2 Due Thursday
May 4
Assignment #2 due
Neural networks, ConvNets
[Assignment #2]
Midterm Tuesday
May 9
In-class midterm
Lecture 11 Thursday
May 11
Detection and Segmentation
Semantic segmentation
Object detection
Instance segmentation
Lecture 12 Tuesday
May 16
Visualizing and Understanding
Feature visualization and inversion
Adversarial examples
DeepDream and style transfer
DeepDream, neural-style
Milestone Tuesday
May 16
Course Project Milestone due
Lecture 13 Thursday
May 18
Unsupervised Learning
Variational Autoencoders
Generative Adversarial Networks
Guest Lecture Tuesday
May 23
Invited Talk
A3 Due Tuesday
May 23
Assignment #3 due [Assignment #3]
Lecture 14 Thursday
May 25
Deep Reinforcement Learning
Policy gradients, hard attention
Q-Learning, Actor-Critic
Lecture 15 Tuesday
May 30
Real-World Use
Convolution algorithms, CPU / GPU
Low-precision, model compression
Lecture 16 Thursday
June 1
Student spotlight talks, conclusions [slides]
Poster Presentation Week of
June 5
Final Project Due Monday
June 12
Final course project due date [reports]