| Event Type | Date | Description | Course Materials |
|---|---|---|---|
| Lecture | Tuesday, Sep 5 | Intro to deep learning, Historical context |
[lecture recording] [slides] [python/numpy tutorial] [software setup for assignments] |
| Lecture | Thursday, Sep 7 | Image classification and the data-driven approach k-nearest neighbor Linear classification |
[lecture recording] [slides] [image classification notes] [linear classification notes] |
| Lecture | Tuesday, Sep 12 | Loss functions Optimization |
[lecture recording] [slides] |
| Lecture | Thursday, Sep 14 |
Optimization Stochastic gradient descent Backproagation |
[lecture recording] [slides] [backprop notes] [Efficient BackProp] (optional) related: [1], [2], [3] (optional) |
| Optional discussion | Friday, Sept. 15 | Python setup, Google collab, Basics of Python and NumPY | |
| Lecture | Tuesday, Sep 19 |
Backpropagation Vector, Matrix, and Tensor Derivatives |
[lecture recording] [slides] handout 1: Vector, Matrix, and Tensor Derivatives handout 2: Derivatives, Backpropagation, and Vectorization Deep Learning [Nature] (optional) |
| Lecture | Thursday, Sep 21 |
Neural networks Training neural networks Activation functions |
[lecture recording] [slides] [Neural Nets notes 1] tips/tricks: [1], [2] (optional) |
| Optional discussion | Friday, Sept. 22 | Reviewing the chain rule, Applying the chain rule to vectors | [slides] |
| Lecture | Tuesday, Sep 26 |
Training neural networks II Weight initialization Batch normalization |
[lecture
recording] [slides] [Neural Nets notes 2] [Batch Norm] |
| Lecture | Thursday, Sep 28 |
Training neural networks III Babysitting the learning process Hyperparameter optimization |
[lecture recording] [slides] [Bengio 2012] (optional) |
| Optional discussion | Friday, Sept. 29 | Vector, Matrix, and Tensor Derivatives | [slides] |
| Lecture | Tuesday, Oct 3 |
Project discussion led by TAs Expectations and timeline Overview of projects outlined by the TAs |
[slides] |
| Thursday, Oct 5 | Instructors at ICCV, no class | ||
| Tuesday, Oct 10 | Monday schedule, no class | ||
| Lecture | Thursday, Oct 12 |
Training neural networks IV Model ensembles, dropout Convolutional neural networks |
[lecture recording] [slides] |
| Optional discussion | Friday, Oct. 13 | Batch normalization |
[slides] |
| Lecture | Tuesday, Oct 17 |
Convolutional neural networks |
[lecture
recording] [slides] ResNet (optional) FCN (optional) |
| Lecture | Thursday, Oct 19 |
ConvNets for spatial localization I Object detection |
[lecture recording] [slides] |
| Lecture | Tuesday, Oct 24 |
ConvNets for spatial localization II Image Segmentation |
[lecture recording] [slides] |
| Lecture | Thursday, Oct 26 | Visualizing and understanding ConvNets |
[lecture recording] [slides] [visualization notes] |
| Lecture | Tuesday, Oct 31 | Neural Texture Synthesis and Style Transfer |
[lecture recording] [slides] |
| Lecture | Thursday, Nov 2 |
Guest lecture: James Tompkin, MLFL (12-1pm, CS 150) More Cameras and Better Cameras for Scene Reconstruction |
[talk info] |
| Lecture | Tuesday, Nov 7 |
Guest lecture: Sajjad Amini Generative modeling: Autoregressive, Variational, GANs |
[lecture recording] [slides] [GAN Notes] |
| Lecture | Thursday, Nov 9 | Recurrent neural networks |
[lecture recording] [slides] |
| Lecture | Tuesday, Nov 14 | Midterm review led by TAs |
[lecture recording] [slides] |
| Midterm | Thursday, Nov 16 | Midterm to be held during regular lecture time | |
| Lecture | Tuesday, Nov 21 | Transformers | [lecture recording] [slides] |
| Lecture | Thursday, Nov 23 | Thanksgiving, no class | |
| Lecture | Tuesday, Nov 28 | Multi-modal AI |
[lecture recording] [slides] |
| Lecture | Thursday, Nov 30 |
Guest lecture: Sara Beery, MLFL (12-1pm, CS150) Monitoring the Urban Forest with Auto Arborist |
|
| Lecture | Tuesday, Dec 5 | Neuro-Symbolic AI |
[lecture recording] [slides] |
| Lecture | Thursday, Dec 7 | Self-Supervised Learning |
[lecture recording] [slides] |