| 1 |
28-Jan |
Lecture 1: Intro + Review of 109A Preview of 109B |
30-Jan |
Lecture 2: Smoothing and Additive 1/3 |
Lab 1: Setting up enviroment |
|
|
| 2 |
4-Feb |
Lecture 3: Smoothing and Additive 2/3 |
6-Feb |
Lecture 4: Smoothing and GAM 3/3 |
Lab 2: Smoothing/GAM |
|
HW1 (2/3) |
| 3 |
11-Feb |
Lecture 5: Feed Forward + Reg + Review from NN fall |
13-Feb |
Lecture 6: Optimization of NN (Solvers) |
Lab 3: Optimization |
Advanced Section 1: Optimization/Dropout |
HW2 (2/10) |
| 4 |
18-Feb |
Holiday |
20-Feb |
Lecture 7: AWS scalable systems SQL |
Lab 4: Setting UP AWS |
Advanced Section 2: Optimal Transport |
|
| 5 |
25-Feb |
Lecture 8: CNNs-1 |
27-Feb |
Lecture 9: CNNs-2 |
Lab 5: CNNs |
Advanced Section 3: CNNs and Object Detection |
HW3 (2/24) |
| 6 |
4-Mar |
Lecture 10: RNN 1 |
6-Mar |
Lecture 11: RNN 2 |
Lab 6: RNNS |
Advanced Section 4: RNNs |
HW4 (3/3) |
| 7 |
11-Mar |
Lecture 12: Unsupervised learning/clustering 1 |
13-Mar |
Lecture 13: Unsupervised learning/clustering 2 |
Lab 7: Clusterig |
Advanced Section 5: Neural Style Transfer |
HW5 (3/10) |
| 8 |
25-Mar |
Lecture 14: Reinforcement Learning |
27-Mar |
Lecture 15: Bayesian 1/3 |
Lab 8: Bayes 1 |
Advanced Section 6: Deep RL |
|
| 9 |
1-Apr |
Lecture 16: Bayesian 2/3 |
3-Apr |
Lecture 17: Bayesian 3/3 |
Lab 9: Bayes 2 |
|
HW6 (3/30) |
| 10 |
8-Apr |
Lecture 18: Generative Models Varational Autoenders 1 |
10-Apr |
Lecture 19: Generative Models Varational Autoenders 2 |
Lab 10: VAE |
Advanced Section 7: Variational Inference |
HW7 (4/7) |
| 11 |
15-Apr |
Lecture 20: GANS |
17-Apr |
Lecture 21: GANS 2 |
Lab 11: Adveserial Networks |
Advanced Section 8: GANS |
|