Schedule

Wk Date Description Materials Deadlines
1 Tue, Mar 31 Lecture 1: Introduction & History slides recording
Thu, Apr 2 Lecture 2: Image Classification with Linear Classifiers slides recording
Fri, Apr 3 Recitation: Broadcasting + Matrix Calculus slides recording
2 Tue, Apr 7 Lecture 3: Regularization and Optimization slides recording A0 Due (Wed)
Thu, Apr 9 Lecture 4: Neural Networks and Backpropagation slides recording
Fri, Apr 10 Recitation: Backpropagation slides recording
3 Tue, Apr 14 Lecture 5: Convolutional Neural Networks (CNNs) slides recording
Thu, Apr 16 Lecture 6: Activations, Normalizations, Optimizers slides A1 Due
Fri, Apr 17 Recitation: CNNs + Vectorization
4 Tue, Apr 21 Lecture 7: Vision and Language Tokenization
Thu, Apr 23 Lecture 8: RNNs & LSTMs
Fri, Apr 24 Recitation: Exam Review
5 Tue, Apr 28 Midterm A2 Due (Mon)
Thu, Apr 30 Lecture 10: Attention and Transformers Proposal Due
Fri, May 1 Recitation: Who's Who of Deep Learning
6 Tue, May 5 Lecture 11: Modern Architectures
Thu, May 7 Lecture 12: Structured Prediction
Fri, May 8 Recitation: Research 101 (and maybe video understanding) A3 Due (Mon)
7 Tue, May 12 Lecture 13: Self-supervised Learning
Thu, May 14 Lecture 14: Foundation Models – Language
Fri, May 15 Recitation: TBD
8 Tue, May 19 Lecture 15: Foundation Models – Multimodal
Thu, May 21 Lecture 16: Interpretability Milestone Due (Fri)
Fri, May 22 Recitation: Generative world models A4 Due (Sat)
9 Tue, May 26 Lecture 17: Reinforcement Learning and Agents
Thu, May 28 Lecture 18: Generative Models – Autoregressive & Quiz
Fri, May 29 Recitation: Building web agents
10 Tue, Jun 2 Lecture 19: VAEs and GANs
Thu, Jun 4 Lecture 20: Diffusion
Fri, Jun 5 Recitation: None A5 Due (Sat)
FE Mon, Jun 8 Poster Presentation Final Report Due
Lecture Recitation Exam Poster Session