| 1 |
Lecture 1: Introduction + Smoothers and Additive 1/3 |
Lecture 2: Smoothers and Additive 2/3 |
Lab 1: Getting Started |
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HW1 - R: 1/29 D: 2/6 |
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| 2 |
Lecture 3: Smoothers and GAM 3/3 |
Lecture 4: Unsupervised learning/clustering 1 |
Lab 2: Smoothers+GAM |
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HW2 - R: 2/5 D: 2/20 |
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| 3 |
Lecture 5: Unsupervised learning/clustering 2 |
Lecture 6: Bayesian 1/3 |
Lab 3: Clustering |
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No New Assignment |
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| 4 |
No Lecture (Presidents' Day) |
Lecture 7: Bayesian 2/3 |
No Lab (President's Day) |
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HW3 - R: 2/19 D: 3/5 |
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| 5 |
Lecture 8: Bayesian 3/3 |
Lecture 9: ML/NN Roadmap |
Lab 4: Bayesian |
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No New Assignment |
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| 6 |
Lecture 10: CNN-1 |
Lecture 11: CNN-2 |
Lab 5: CNNs-1 |
Advanced Section 1: ResNet, Dense-Net, res-Next and Inception and transfer learning |
HW4 - R: 3/4 D: 3/12 |
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| 7 |
Lecture 12: Autoencoders + Unet |
Lecture 13: RNNs 1 |
Lab 6: CNNs-2 |
Advanced Section 2: Segmentation Techniques, YOLO, Unet and M-RCNN |
HW5 - R: 3/11 D: 3/26 |
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| 8 |
No Lecture (Spring Break) |
No Lecture |
No Lab |
No Advanced Section |
No New Assignment |
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| 9 |
Lecture 14: RNNs 2 |
Lecture 15: RNNs 3 |
Lab 7: AE |
Advanced Section 3: RNN, echo state |
HW6 - R: 3/25 D: 4/9 |
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| 10 |
Lecture 16: Language Models 1 |
Lecture 17: Language Models 2 |
Lab 8: RNNS |
No Advanced Section |
No New Assignment |
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| 11 |
Lecture 18: VAE + Generative Models + GANs 1 |
Lecture 19: GANS 2 |
Lab 9: Text |
Advanced Section 4: Variational Inference |
HW7 - R: 4/8 D: 4/16 |
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| 12 |
Lecture 20: Reinforcement Learning Basics |
Lecture 21: Deep Reinforcement Learning |
Lab 10: VAE+GANS |
Advanced Section 5: RL |
HW8 - R: 4/15 D: 4/23 |
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| 13 |
MODULE: LECTURE DOMAIN |
MODULE: PROBLEM BACKGROUND |
Lab 11: RL |
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| 14 |
MODULE |
PROJECT WORK |
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| 15 |
PROJECT WORK |
PROJECT WORK |
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