Tentative Class Schedule
(Files available through Canvas Login)
Lecture 1 |
Course Introduction |
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Lecture 2 |
Neural Network Basics |
HW 1 Out |
Lecture 3 |
Optimization for Training Deep Models |
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Lecture 4 |
Convolutional Networks |
HW 2 Out |
Lecture 5 |
Recurrent Neural Networks |
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Lecture 6 |
Encoder-Decoder Architectures |
HW 3 Out |
Lecture 7 |
Deep Reinforcement Learning |
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Lecture 8 |
Autoencoders |
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Lecture 9 |
Deep Generative Models |
HW 4 Out |
Lecture 10 |
Adversarial Training |
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Lecture 11 |
Deep Learning Applications I (Computer Vision) |
HW 5 Out |
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Lecture 12 |
Deep Learning Applications II (Text Analytics) |
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Project Presentations |
Project Report Due |
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