--- layout: page_2023 title: Schedule year: 2023 permalink: /2023/schedule/ --- Lecture slides and Jupyter notebooks can be found [here](https://drive.google.com/drive/folders/117sLpWGyqBjMILgibwnofzY0adactYiM?usp=sharing).

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)