Event Type | Date | Description | Course Materials | |
---|---|---|---|---|
Lecture | Tuesday, Sep 3 | Course logistics and overview Historical context |
[slides] [python/numpy tutorial] [software setup for assignments] |
|
Lecture | Thursday, Sep 5 | Image classification and the data-driven approach
|
[slides] [image classification notes] [linear classification notes] |
|
Lecture | Tuesday, Sep 10 | Optimization
|
[slides] |
|
Lecture | Thursday, Sep 12 |
Optimization:
|
[slides] [optimization and sgd notes] |
|
Optional discussion | Friday, Sep 13, 11-12pm, CS 142 | Python setup, Google collab, Basics of Python and Numpy | [Notes] | |
Lecture | Tuesday, Sep 17 |
Learning rate schedules Neural networks |
[slides] |
|
Lecture | Thursday, Sep 19 |
Backpropagation Vector, matrix, and tensor derivatives |
[slides] [backprop notes] handout 1: vector, matrix, and tensor derivatives handout 2: derivatives, backpropagation, and vectorization [efficient backprop] (optional) related: [1], [2], [3] (optional) |
|
Optional discussion | Friday, Sep 20, 11-12pm, CS 142 | Reviewing the chain rule, Applying the chain rule to vectors | [slides] | |
Lecture | Tuesday, Sep 24 |
Training neural networks Activation Functions |
[slides] |
|
Lecture | Thursday, Sep 26 |
Training neural networks II Weight initialization Batch normalization |
[slides] [neural nets notes 2] [batch norm] |
|
Optional discussion | Friday, Sep 27, 11-12pm, CS 142 | Vector, Matrix, and Tensor Derivatives | [slides] | |
Lecture | Tuesday, Oct 1 |
Project I Expectations and timeline Overview of projects by the TAs |
[slides 1] [slides 2] [project ideas] |
|
Lecture | Thursday, Oct 3 |
Project II Work on project proposal in class |
||
Optional discussion | Friday, Oct 4, 11-12pm, CS 142 | Batch normalization |
[slides] |
|
Lecture | Tuesday, Oct 8 |
Training neural networks III Hyperparameter optimization Model ensembles, dropout Convolutional neural networks |
[slides] | |
Lecture | Thursday, Oct 10 |
Guest lecture: Xiaolong Wang (UIUC) |
||
Tuesday, Oct 15 |
No class, Monday schedule |
|||
Lecture | Thursday, Oct 17 |
Convnets for spatial localization I: Object detection |
[slides] | |
Lecture | Tuesday, Oct 22 |
Convnets for spatial localization II: Image segmentation |
[slides] | |
Lecture | Thursday, Oct 24 |
Guest Lecture: Alex Wong (Yale) Title: The know-how of multimodal depth perception |
||
Lecture | Tuesday, Oct 29 |
Understanding and visualizing convnets |
[slides] [lecture recording, 2023]; this year's version has missing audio [visualization notes] |
|
Lecture | Thursday, Oct 31 |
Guest lecture: Boqing Gong (Google, BU) Title: From domain adaptation to videoprism |
||
Tuesday, Nov 5 |
No class, Election Day |
|||
Lecture | Thursday, Nov 7 | Adversarial examples, Neural texture synthesis and style transfer |
[slides] |
|
Lecture | Tuesday, Nov 12 | Recurrent neural networks | [slides] | |
Lecture | Thursday, Nov 14 | Transformers | [slides] | |
Lecture | Tuesday, Nov 19 | Self-supervised learning |
[slides] |
|
Lecture | Thursday, Nov 21 |
Guest lecture: Chen Sun (Brown) Title: Grounding deep generative models in the physical world |
||
Lecture | Tuesday, Nov 26 | No regular class, work on projects | ||
Thursday, Nov 28 | No class, Thanksgiving Break | |||
Project Presentation | Tuesday, Dec 3 | Group 1 | ||
Project Presentation | Thursday, Dec 5 | Group 2 | ||
Project Presentation | Tuesday, Dec 10 | Group 3 |