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 be Fridays 12:30pm to 1:20pm in Skilling Auditorium.
(map)
This is the syllabus for the Spring 2018 iteration of the course.
The syllabus for the Spring 2017, Winter 2016
and Winter 2015 iterations of this course
are still available.
Event Type  Date  Description  Course Materials 

Lecture 1  Tuesday April 3 
Course Introduction Computer vision overview Historical context Course logistics 
[slides] 
Lecture 2  Thursday April 5 
Image Classification The datadriven approach Knearest neighbor Linear classification I 
[slides]
[python/numpy tutorial] [image classification notes] [linear classification notes] 
Discussion Section  Friday April 6 
Python / numpy / Google Cloud  [python/numpy notebook] 
Lecture 3  Tuesday April 10 
Loss Functions and Optimization Linear classification II Higherlevel representations, image features Optimization, stochastic gradient descent 
[slides]
[linear classification notes] [optimization notes] 
Lecture 4  Thursday April 12 
Introduction to Neural Networks Backpropagation Multilayer 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 13 
Backpropagation  [slides] 
Lecture 5  Tuesday April 17 
Convolutional Neural Networks History Convolution and pooling ConvNets outside vision 
[slides]
ConvNet notes 
A1 Due  Wednesday April 18 
Assignment #1 due kNN, SVM, SoftMax, twolayer network 
[Assignment #1] 
Lecture 6  Thursday April 19 
Training Neural Networks, part I Activation functions, initialization, dropout, batch normalization 
[slides]
Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: [1], [2], [3] (optional) Deep Learning [Nature] (optional) 
Lecture 7  Tuesday April 24 
Training Neural Networks, part II Update rules, ensembles, data augmentation, transfer learning 
[slides]
Neural Nets notes 3 
Proposal due  Wednesday April 25 
Project Proposal due  [proposal description] 
Lecture 8  Thursday April 26 
Deep Learning Software Caffe, Torch, Theano, TensorFlow, Keras, PyTorch, etc 
[slides] 
Lecture 9  Tuesday May 1 
CNN Architectures AlexNet, VGG, GoogLeNet, ResNet, etc 
[slides]
AlexNet, VGGNet, GoogLeNet, ResNet 
A2 Due  Wednesday May 2 
Assignment #2 due Neural networks, ConvNets 
[Assignment #2] 
Lecture 10  Thursday May 4 
Recurrent Neural Networks RNN, LSTM, GRU Language modeling Image captioning, visual question answering Soft attention 
[slides]
DL book RNN chapter (optional) mincharrnn, charrnn, neuraltalk2 
Midterm  Tuesday May 8 
Inclass midterm Location: TBD (not our usual classroom) 
SCPD Midterm Info 
Lecture 11  Thursday May 10 
Detection and Segmentation Semantic segmentation Object detection Instance segmentation 
[slides]

Lecture 12  Tuesday May 15 
Visualizing and Understanding Feature visualization and inversion Adversarial examples DeepDream and style transfer 
[slides]
DeepDream neuralstyle fastneuralstyle 
Milestone  Wednesday May 16 
Project Milestone due  
Lecture 13  Thursday May 17 
Generative Models PixelRNN/CNN Variational Autoencoders Generative Adversarial Networks 
[slides]

Lecture 14  Tuesday May 22 
Deep Reinforcement Learning Policy gradients, hard attention QLearning, ActorCritic 
[slides]

A3 Due  Wednesday May 23 
Assignment #3 due  [Assignment #3] 
Lecture 15  Thursday May 24 
Topic TBD 
[slides]

Lecture 16  Tuesday May 29 
Topic TBD

[slides]

Lecture 17  Thursday May 31 
Student spotlight talks, conclusions  [slides] 
Final Project Due  Tuesday June 5  Project Report due  
Poster Session  Tuesday June 12 