1
Tue, April 1
Lecture 1:
Introduction
Thu, April 3
Lecture 2:
Image Classification with Linear Classifiers
Fri, April 4
Recitation:
Fundamentals
2
Tue, April 8
Lecture 3:
Regularization and Optimization
A0 Due
Thu, April 10
Lecture 4:
Neural Networks and Backpropagation
Fri, April 11
Recitation:
Project Design & Backprop
3
Tue, April 15
Lecture 5:
Convolutional Neural Networks (CNNs)
A1 Due (Wed)
Thu, April 17
Lecture 6:
Activation Functions & Normalization Layers
Fri, April 18
Recitation:
Convolutions & Vectorization
W1 Due
4
Tue, April 22
Lecture 7:
Optimizers
Thu, April 24
Lecture 8:
Interpretability
Fri, April 25
Recitation:
Quantization
A2 Due (Sun)
5
Tue, April 29
Lecture 9:
Vision and Language Tokenization
Proposal Due
Thu, May 1
Lecture 10:
RNNs & LSTMs
Fri, May 2
Recitation:
Who's Who of Deep Learning
6
Tue, May 6
Lecture 11:
Attention and Transformers
Thu, May 8
Lecture 12:
Modern Architectures
Fri, May 9
Recitation:
Scaling Laws
A3 Due (Sun)
W2 Due
7
Tue, May 13
Lecture 13:
Structured Prediction
Thu, May 15
Lecture 14:
Self-Supervised Learning
Milestone Due
Fri, May 16
Recitation:
Exam Review
Thu, May 22
Lecture 15:
Foundation Models - Language
Fri, May 23
Recitation:
LoRA
A4 Due
9
Tue, May 27
Lecture 16:
RLHF & DPO
Thu, May 29
Lecture 17:
Foundation Models - Multimodal
Fri, May 30
Recitation:
Flash Attention
W Early Draft Due
10
Tue, June 3
Lecture 18:
Autoregressive & VAEs
Thu, June 5
Lecture 19:
GANs & Diffusion
Fri, June 6
Recitation:
None
A5 Due
W3 Due
Finals
Mon, June 9
Poster Session
Allen Center Atrium
Final Report Due
W Revisions Due