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CSE 4095/5095
Deep Learning
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(Tentative schedule for the course and deviations may be necessary as the course progresses.)
| Date |
Topic |
Notes |
Reading (Textbook or Other Materials) |
| Jan. 21 |
Introduction |
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Chapter 1, 2, 3, Nature review on DL, Basic calculus refresher |
| Jan. 23 |
Machine Learning Basics |
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Chapter 4, 5, Machine learning math essentials |
| Jan. 28 |
Shallow Classifiers |
Project Details |
Chapter 4, 5 |
| Jan. 30 |
Loss Functions |
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Chapter 4, 5 |
| Feb. 4 |
Optimization, SGD |
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Chapter 4, 5 |
| Feb. 11 |
Backpropagation |
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Chapter 4, 5 |
| Feb. 13 |
Intro to Neural Networks |
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| Feb. 18 |
Intro to Convolutional Neural Networks, Convolution |
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AlexNet |
| Feb. 20 |
Pooling, Demo, BP example |
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All Convolutional Net |
| Feb. 25 |
Training Neural Networks |
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| Feb. 27 |
Training Neural Networks II, Batch Normalization |
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Batch Normalization |
| Mar. 4 |
Second-order Optimization |
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Chapter 8 |
| Mar. 6 |
Regularization, Transfer Learning |
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Dropout, Blackout, DeCAF |
| Mar. 11 |
ImageNet, CNN Architectures |
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AlexNet, VGGNet, GoogleNet, ResNet |
| Mar. 13 |
CNN Architectures and Visualization |
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Wide ResNet, Zeiler and Fergus, Yosinski et al. |
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