(Tentative schedule for the course and deviations may be necessary as the course progresses.)
| Date |
Topic |
Notes |
Reading (Textbook or Other Materials) |
| Aug. 21 |
Introduction |
|
Chapter 1, 2, 3, Nature review on DL, Basic calculus refresher |
| Aug. 23 |
Machine Learning Basics |
|
Chapter 4, 5, Machine learning math essentials |
| Aug. 28 |
Shallow Classifiers |
Project Details |
Chapter 4, 5 |
| Aug. 30 |
Loss Functions |
|
Chapter 4, 5 |
| Sept. 6 |
Optimization, SGD |
|
Chapter 4, 5 |
| Sept. 11 |
Backpropagation |
|
Chapter 4, 5 |
| Sept. 13 |
Intro to Neural Networks |
|
|
| Sept. 18 |
Intro to Convolutional Neural Networks, Convolution |
|
AlexNet |
| Sept. 20 |
Pooling, Demo, BP example |
|
All Convolutional Net |
| Sept. 25 |
Training Neural Networks |
|
|
| Sept. 27 |
Training Neural Networks II, Batch Normalization |
|
Batch Normalization |
| Oct. 2 |
Second-order Optimization |
|
Chapter 8 |
| Oct. 4 |
Regularization, Transfer Learning |
|
Dropout, Blackout, DeCAF |
| Oct. 9 |
ImageNet, CNN Architectures |
|
AlexNet, VGGNet, GoogleNet, ResNet |
| Oct. 11 |
CNN Architectures and Visualization |
|
Wide ResNet, Zeiler and Fergus, Yosinski et al. |
| Oct. 16 |
Word2Vec |
|
word2vec, glove, wordrank |
| Oct. 18 |
Doc2Vec, RNNLM |
|
doc2vec, rnnlm |
| Oct. 23 |
LSTM, GRU |
|
Chapter 10 |
| Oct. 25 |
Image Caption, Machine Translation |
|
Show, Attend and Tell, NMT with Attention |
| Oct. 30 |
Transformer |
|
Attention is All You Need, The Illustrated Transformer |
| Nov. 1 |
Unsupervised Learning, PCA, AutoEncoder |
|
Chapter 13, 14 |
| Nov. 6 |
Variational AutoEncoder |
|
VAE |
| Nov. 8 |
Generative Adversarial Networks |
|
GANs, DCGANs |
| Nov. 13 |
Review |
|
|
| Nov. 15 |
Final Exam |
|
|
| Nov. 27 |
Presentation |
|
|
| Nov. 29 |
Presentation |
|
|
| Dec. 4 |
Presentation |
|
|
| Dec. 8 |
Project Report Due (by 11:59 pm) |
|
|
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