Lectures are held at WTS A30 (Watson Center) from 9:00am to 11:15m on Monday (starting on Jan 13, 2020). The second lecture is from 9:00am to 11:15am on Friday (Jan 17, 2020).
Instructors: Dr. Martin Renqiang Min and Prof. Mark Gerstein.
A detailed list of topics to be covered is here.
Technical sections: Friday 9:00am  10:00am DL 120 (Dunham Laboratory).
Teaching Fellow: Yitan Wang.
Textbook: There is no required textbook for this course, but there are several freely available online textbooks for your reference.
1. Deep Learning.
2. Dive into Deep Learning.
3. Neural Networks and Deep Learning.
4. Pattern Recognition and Machine Learning [pdf].
A closely related course focusing on computer vision was offered at Stanford (cs231n).
All the listed future dates didn't consider spring break and will be finalized soon.
Event Type  Date  Description  Course Materials 

Lecture 1  Monday Jan 13 
Course Introduction Introduction to neural networks, backpropagation, and deep learning Course logistics 
[slides]
Required Reading: [backpropagation] Deep Learning Review Paper [Nature] Optional Reading: [backprop notes] [Efficient BackProp] related: [1], [2], [3] 
Lecture 2  Friday Jan 17 
Supervised Deep Learning Activation functions, Convoutional Neural Networks Image Classification, AlexNet, VGG, ResNet pretraining, naive transfer learning network visualization, deep dream, style transfer 
[slides]
Required Reading: Deep Learning Review Paper [Nature] AlexNet Optional Reading: [python/numpy tutorial] [image classification notes] [linear classification notes] VGGNet, GoogLeNet, ResNet 
Technical Section  Friday Jan 24 
Python/numpy/Deep Learning Hardware/Software 
[Numpy notebook] [PyTorch notebook] Stanford cs231n 2017 YouTube Lecture 8 
Lecture 3  Monday Jan 27 
Optimization, Regularization, and Robustness Optimization and regularization methods Adversarial examples and robust optimization Attack and defense methods 
[slides]
Optional Reading: SGD by Leon Bottou [cs231n optimization note 1] [cs231n optimization note 2] [cs231n optimization note 3] Robust optimization against adversarial 
A1 Posted  Wednesday Jan 29 
Assignment 1 perceptron, backpropagation, optimization programming: experimenting with activation functions, different layers, loss functions, gradient vanishing, and optimization methods 
[A1 Written Part] 
Technical Section  Friday Jan 31 
PyTorch and CNN Filter Visualization PyTorch tutorials on Autograd Training a simple CNN and a classifier CNN filter visualization DeepDream and Style Transfer 
Neural Network in PyTorch Classifier in PyTorch Stanford 2017 cs231n YouTube Lecture 12 
Lecture 4  Monday Feb 3 
Recurrent Neural Networks LSTM, GRU 
[slides]
Optional Reading: DL book RNN chapter mincharrnn 
Project Topics  Thursday Feb 6 
Some possible project topics  [Some Topics] 
Technical Section  Friday Feb 7 
Adversarial Examples 
[Stanford 2017 cs231n YouTube Lecture 16] 
A1 Coding Part  Saturday Feb 8 
A1 Coding Posted experimenting with activation functions, shallow and deep architectures, dropout, gradient vanishing, and optimization methods 
[A1 Coding Part] [A1 Starter Code] 
Lecture 5  Monday Feb 10 
Deep Autoencoder RBM, Autoencoder 
[slides]
Required Reading: Dimensionality Reduction Application Denoising Autoencoder Optional Reading: Ladder Network tips/tricks: [1], [2] 
Proposal Title due  Monday Feb 10 
Project abstract and team formation due  [Stanford 2017 cs231n proposal description] 
Lecture 6  Monday Feb 17 
Deep EncoderDecoder Networks and Applications Word embedding Machine translation Image and video captioning 
[slides] Required Reading: Seq2Seq Optional Reading: [Stanford 2017 cs231n YouTube Lecture 11] Word2Vec 
Lecture 7  Monday Feb 24 
Attention Mechanisms and Applications Neural Machine Translation Question Answering Transformer and BERT 
[slides] Required Reading: NMT with Attention Optional Reading: Transformer BERT Interactive QA 
A2 Posted  Thursday Feb 27 
Assignment #2 posted Understand exploding and vanishing gradient of vanilla RNN, understand RBM and autoencoder PyTorch with DNN, CNN, vanilla RNN, LSTM/GRU 
[Assignment #2] [Starter Code (163M)] 
Lecture 8  Monday Mar 2 
Deep Probabilistic Generative Models: Variational Inference and Variational Autoencoder (Reweighted) WakeSleep Algo (Neural) VI, VAE, PixelCNN 
[slides] Required Reading: VAE Optional Reading: Neural VI 
Lecture 9 (Zoom)  Monday Mar 23 
Deep Generative Models: Generative Adversarial Networks GAN, Conditional GAN CycleGAN, Domain Adaptation Wasserstein distance, WGAN Video Generation, Text2Video 
[slides] Required Reading: GAN Optional Reading: Wasserstein GAN Text2Video CycleGAN [Stanford 2017 cs231n YouTube Lecture 13] 
A2 Due  Friday Mar 27 
Assignment #2 due Understand exploding and vanishing gradient of vanilla RNN, understand RBM and autoencoder PyTorch with DNN, CNN, vanilla RNN, LSTM/GRU 
[Assignment #2] 
A3 Posted  Saturday Mar 28 
Assignment #3 posted Understand issues of VAE and GAN Train VAE or GAN on MNIST 
[Assignment #3] 
Lecture 10 (Zoom)  Monday Mar 30 
Deep Reinforcement Learning Deep QLearning Policy Gradient, ActorCritic 
[Part1]
[Part2]
Required Reading: AlphaGO Optional Reading: Stanford cs231n 2017 Lecture 14 AlphaZero ICML 2017 DRL Tutorial 
Lecture 11  Monday Apr 6 
Biomedical Application Case Study I 
[slides]

Lecture 12  Monday Apr 13 
Biomedical Application Case Study II 
[Part1]
[Part2]

Guest Lecture by Renjie Liao  Friday Apr 17 
Graph Neural Networks  Renjie Liao 
Lecture 13  Monday Apr 20 
Biomedical Application Case Study III 
[slides]

Guest Lecture by Dr. Asim Kadav  Friday Apr 24 
Video Understanding  [Slides] 
A3 Due  Monday Apr 27 
Assignment #3 due Understand issues of VAE and GAN Train VAE or GAN on MNIST 
[Assignment #3] 
Paper Summary Due  Thursday Apr 30 
Paper Summary due 10 Required Papers 

Final Project Paper Due  Thursday 7 May 
Project Paper due  
Presentation Video Due  7 May  Video No More Than 5 Minutes 