1
Thu, Sep 26
Lecture 1:
Introduction
Fri, Sep 27
Recitation:
Broadcasting & Chain Rule
2
Tue, Oct 1
Lecture 2:
Image Classification with Linear Classifiers
Thu, Oct 3
Lecture 3:
Regularization and Optimization
A0 due
Fri, Oct 4
Recitation:
Project Design & Vectorization
3
Tue, Oct 8
Lecture 4:
Neural Networks and Backpropagation
Thu, Oct 10
Lecture 5:
Convolutional Neural Networks (CNNs)
A1 due Oct 13, 11:59 PM
Fri, Oct 11
Recitation:
Quiz 1
4
Tue, Oct 15
Lecture 6:
Training Neural Networks (Part 1)
Thu, Oct 17
Lecture 7:
Training Neural Networks (Part 2)
Fri, Oct 18
Recitation:
Backprop & Convolutions
5
Tue, Oct 22
Lecture 8:
Visualizing and Understanding
A2 due
Thu, Oct 24
Lecture 9:
Vision and Language Tokenization
Fri, Oct 25
Recitation:
Quiz 2
6
Tue, Oct 29
Lecture 10:
RNNs & LSTMs
Proposal due
Thu, Oct 31
Lecture 11:
Attention and Transformers
Fri, Nov 1
Recitation:
Pytorch / Tensorflow tutorial
7
Tue, Nov 5
Lecture 12:
Modern Architectures
A3 due
Thu, Nov 7
Lecture 13:
Transfer learning
Structured prediction (Detection + Segmentation)
Fri, Nov 8
Recitation:
Quiz 3
8
Tue, Nov 12
Lecture 14:
Self-supervised learning
Thu, Nov 14
Lecture 15:
Foundation Models - LMs
A4 due
Fri, Nov 15
Recitation:
Fine-tuning
9
Tue, Nov 19
Lecture 16:
Foundation Models - Multimodal
Milestone due
Thu, Nov 21
Lecture 17:
Autoregressive & VAEs
Fri, Nov 22
Recitation:
Quiz 4
10
Tue, Nov 26
Lecture 18:
GANs + Diffusion (Part 1)
Fri, Nov 29
THANKSGIVING
A5 due Dec 1, 11:59pm
11
Tue, Dec 3
Lecture 19:
Quiz 5 + Diffusion (Part 2)
Quiz 5
Thu, Dec 5
Lecture 20:
Poster session