Schedule
Lecture slides and Jupyter notebooks can be found here.
Lecture 1 | Jan. 30 | overview, simple perceptrons |
Lecture 2 | Feb. 1 | stochastic gradient descent |
Pset 0 optional | Feb. 3 | math review |
Lecture 3 | Feb. 6 | multilayer perceptrons |
Lecture 4 | Feb. 8 | convolution and pooling |
Pset 1 due | Feb. 10 | perceptrons |
Lecture 5 | Feb. 13 | visual object recognition |
Lecture 6 | Feb. 15 | generalization & regularization |
Pset 2 due | Feb. 17 | convolutional nets |
Exam | Feb. 20 | |
Lecture 7 | Feb. 22 | exam retrospective |
Lecture 8 | Feb. 27 | BatchNorm, transfer learning |
Lecture 9 | Mar. 1 | object detection |
Pset 3 due | Mar. 3 | object classification/detection |
Lecture 10 | Mar. 6 | deconvolution layers |
Lecture 11 | Mar. 8 | segmentation |
Pset 4 due | Mar. 10 | segmentation and denoising |
Break | ||
Lecture 12 | Mar. 20 | diffusion models |
Lecture 13 | Mar. 22 | attention |
Pset 5 due | Mar. 24 | diffusion models |
Lecture 14 | Mar. 27 | transformers |
Lecture 15 | Mar. 29 | language modeling |
Pset 6 due | Mar. 31 | transformers |
Lecture 16 | Apr. 3 | LLMs |
Exam | Apr. 5 | |
Lecture 17 | Apr. 10 | conditional image generation |
Lecture 18 | Apr. 12 | policy gradient |
Project | Apr. 14 | proposal due |
Lecture 19 | Apr. 17 | prompt engineering, RLHF |
Lecture 20 | Apr. 19 | project meetings |
Project | Apr. 21 | status report |
Lecture 21 | Apr. 24 | AI safety |
Lecture 22 | Apr. 26 | project meetings |
Final project | May 9 | (Dean's date) |