| 24-Jan |
Lecture 1: Clustering 1 |
Lecture 2: Clustering 2 |
Lab 1 |
|
R:HW1 |
| 31-Jan |
Lecture 3: Bayes 1 |
Lecture 4: Bayes 2 |
Lab 2 |
Advanced Section 1: Gaussian Mixture Models |
|
| 7-Feb |
Lecture 5: Bayes 3 |
Lecture 6: Bayes 4 |
Lab 3 |
|
R:HW2 - D:HW1 |
| 14-Feb |
Lecture 7: Bayes 5 |
Lecture 8: Neural Networks 1 (MLP) |
Lab 4 |
Advanced Section 2: Particle Filters/Sequential Monte Carlo |
|
| 21-Feb |
No Lecture (Holiday) |
Lecture 9: NN 2 Gradient Descent; SGD; BackProp) |
Lab 5 |
|
R:HW3 - D:HW2 |
| 28-Feb |
Lecture 10: NN 3 (Optimizers) |
Lecture 11: NN 4 (Regularization) |
Lab 6 |
Advanced Section 3: Solvers |
|
| 7-Mar |
Lecture 12: Convolutional Neural Networks 1 (Basics) |
Lecture 13: CNNs 2 (Regularization) |
Lab 7 |
Advanced Section 4: Segmentation |
R:HW4 - D:HW3 |
| 14-Mar |
No Lecture (Spring Break) |
No Lecture |
No Lab |
|
|
| 21-Mar |
Lecture 14: CNNs 3 (Receptive Field) |
Lecture 15: CNNs 4 (Saliency Map) |
No Lab (Midterm) |
Advanced Section 5: SOTA & Transfer Learning |
|
| 28-Mar |
Lecture 16: Intro to Language Models |
Lecture 17: Recurrent Neural Networks |
Lab 8 |
Advanced Section 6: Autoencoders |
R:HW5 - D:HW4 |
| 4-Apr |
Lecture 18: NLP 1 (GRUs/LSTMs) |
Lecture 19: NLP 2 (ELMO) |
Lab 9 |
Advanced Section 7: Word2Vec |
R:HW6(Individual) |
| 11-Apr |
Lecture 20: NLP 3 (Seq2Seq & Attention) |
Lecture 21: NLP 4 (Transformers) |
Lab 10 |
Advanced Section 8: BERT |
D:HW5 |
| 18-Apr |
Lecture 22: GANs 1 |
Lecture 23: GANs 2 |
Lab 11 |
Advanced Section 9: More GANs! |
D:HW6(Individual) - R:HW7 |
| 25-Apr |
Module: Lecture Domain |
Module: Problem Background |
Project Work |
|
D:HW7 |
| 2-May |
Project Work |
Project Work |
Project Submission Due |
|
|
| 9-May |
Peer Evaluations Due |
|
Final Project Showcase |
|
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