Lecture slides and Jupyter notebooks can be found here.

Lecture 1 Jan. 29 overview, simple perceptrons
Lecture 2 Jan. 31 stochastic gradient descent
Pset 0 optional Feb. 2 math review
Lecture 3 Feb. 5 multilayer perceptrons
Lecture 4 Feb. 7 convolution
Pset 1 due Feb. 9 perceptrons
Lecture 5 Feb. 12 convolutional nets
Lecture 6 Feb. 14 visual object recognition
Pset 2 due Feb. 16 MLPs and convolution
Lecture 7 Feb. 19 generalization & regularization
Exam Feb. 21 Exam 1
Lecture 8 Feb. 26 BatchNorm, transfer learning
Lecture 9 Feb. 28 object detection
Pset 3 due Mar. 1 object classification/detection
Lecture 10 Mar. 4 deconvolution layers
Lecture 11 Mar. 6 segmentation
Pset 4 due Mar. 8 segmentation and denoising
Break    
Lecture 12 Mar. 18 autoencoders and diffusion models
Lecture 13 Mar. 20 language models and attention
Pset 5 due Mar. 22 diffusion models
Lecture 14 Mar. 25 review
Exam Mar. 27 Exam 2
Lecture 15 Apr. 1 transformers
Lecture 16 Apr. 3 GPT
Pset 6 due Apr. 5 transformers
Lecture 17 Apr. 8 in-context learning
Lecture 18 Apr. 10 policy gradient
Project Apr. 12 proposal due
Lecture 19 Apr. 15 prompt engineering, RLHF
Lecture 20 Apr. 17 project meetings
Project Apr. 19 status report
Lecture 21 Apr. 22 AI safety
Lecture 22 Apr. 24 project meetings
Final project May 7 (Dean's date)

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