1
Tue, January 6
Lecture 1:
Introduction & History
Thu, January 8
Lecture 2:
Image Classification with Linear Classifiers
Fri, January 9
Recitation:
Fundamentals
A0 Due (Not Graded)
2
Tue, January 13
Lecture 3:
Regularization & Optimization
Thu, January 15
Lecture 4:
Neural Networks & Backpropagation
Fri, January 16
Recitation:
Backpropagation
3
Tue, January 20
Lecture 5:
Convolutional Neural Networks (CNNs)
Thu, January 22
Lecture 6:
Activation Functions & Normalization Layers
A1 Due
Fri, January 23
Recitation:
Convolutions + Vectorization
W Interest Form Due (Mon)
4
Tue, January 27
Lecture 7:
Optimizers
Thu, January 29
Lecture 8:
Interpretability
Fri, January 30
Recitation:
Quantization
A2 Due (Sun)
5
Tue, February 3
Lecture 9:
Vision and Language Tokenization
Thu, February 5
Midterm I
Fri, February 6
Recitation:
TBD
W1 Due (Mon)
6
Tue, February 10
Lecture 10:
RNNs & LSTMs
Thu, February 12
Lecture 11:
Attention & Transformers
Fri, February 13
Recitation:
Optimizing Attention
7
Tue, February 17
Lecture 12:
Modern Architectures
A3 Due
Thu, February 19
Lecture 13:
Structured Prediction
Fri, February 20
Recitation:
TBD
W2 Due (Mon)
8
Tue, February 24
Lecture 14:
Self-supervised Learning
Thu, February 26
Lecture 15:
Foundation Models – Language
Fri, February 27
Recitation:
Coding Agents
A4 Due
9
Tue, March 3
Midterm II
Thu, March 5
Lecture 16:
Foundation Models – Multimodal
Fri, March 6
Recitation:
Robotics
W Early Draft Due
10
Tue, March 10
Lecture 17:
Image Generation
Thu, March 12
Lecture 18:
Video Generation
Fri, March 13
Recitation:
None
A5 Due
Finals
Mon, March 16
Poster Session
Allen Center Atrium, TBD
W3 Due
W Final Report Due
W Revisions Due