Event Type  Date  Description  Course Materials 

Lecture 1  Tuesday April 4 
Course Introduction Computer vision overview Historical context Course logistics 
[slides] 
Lecture 2  Thursday April 6 
Image Classification The datadriven approach Knearest neighbor Linear classification I 
[slides]
[python/numpy tutorial] [image classification notes] [linear classification notes] 
Lecture 3  Tuesday April 11 
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 13 
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) 
Lecture 5  Tuesday April 18 
Convolutional Neural Networks History Convolution and pooling ConvNets outside vision 
[slides]
ConvNet notes 
Lecture 6  Thursday April 20 
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) 
A1 Due  Thursday April 20 
Assignment #1 due kNN, SVM, SoftMax, twolayer network 
[Assignment #1] 
Lecture 7  Tuesday April 25 
Training Neural Networks, part II Update rules, ensembles, data augmentation, transfer learning 
[slides]
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 
[slides] 
Lecture 9  Tuesday May 2 
CNN Architectures AlexNet, VGG, GoogLeNet, ResNet, etc 
[slides] AlexNet, VGGNet, GoogLeNet, ResNet 
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 
A2 Due  Thursday May 4 
Assignment #2 due Neural networks, ConvNets 
[Assignment #2] 
Midterm  Tuesday May 9 
Inclass midterm Location: Various (not our usual classroom) 

Lecture 11  Thursday May 11 
Detection and Segmentation Semantic segmentation Object detection Instance segmentation 
[slides] 
Lecture 12  Tuesday May 16 
Visualizing and Understanding Feature visualization and inversion Adversarial examples DeepDream and style transfer 
[slides] DeepDream neuralstyle fastneuralstyle 
Milestone  Tuesday May 16 
Course Project Milestone due  
Lecture 13  Thursday May 18 
Generative Models PixelRNN/CNN Variational Autoencoders Generative Adversarial Networks 
[slides] 
Lecture 14  Tuesday May 23 
Deep Reinforcement Learning Policy gradients, hard attention QLearning, ActorCritic 
[slides] 
Lecture 15  Thursday May 25 
RealWorld Use Convolution algorithms, CPU / GPU Lowprecision, model compression 
[slides] 
A3 Due  Friday May 26 
Assignment #3 due  [Assignment #3] 
Guest Lecture  Tuesday May 30 
Invited Talk: Ian Goodfellow 
[slides] 
Lecture 16  Thursday June 1 
Student spotlight talks, conclusions  [slides] 
Poster Due  Monday June 5 
Poster PDF due  [poster description] 
Poster Presentation  Tuesday June 6 

Final Project Due  Monday June 12 
Final course project due date  [reports] 