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 datadriven approach knearest 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 (121pm, 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  Multimodal AI 
[lecture recording] [slides] 
Lecture  Thursday, Nov 30 
Guest lecture: Sara Beery, MLFL (121pm, CS150) Monitoring the Urban Forest with Auto Arborist 

Lecture  Tuesday, Dec 5  NeuroSymbolic AI 
[lecture recording] [slides] 
Lecture  Thursday, Dec 7  SelfSupervised Learning 
[lecture recording] [slides] 