This page provides a tentative schedule of all lectures for the coures. Slides and video recordings will be released during the courses of the semester. Course lectures can be watched usually within 2-3 hours after class lecture, and are available on the course Canvas page (click on “Cloud Recordings” and navigate to the lecture of the appropriate date).

Date	Lecture	Instructor	Slides	Comments
8/30	1 - Introduction / Logistics	Kolter	pdf	HW0 Out
9/1	2 - ML Refresher / Softmax Regression	Kolter	pdf
9/8	3 - Manual Neural Networks / Backprop	Kolter	pdf
9/13	4 - Automatic Differentiation	Chen	pdf	HW0 Due
9/15	5 - AD Implementation	Chen	ipynb	HW1 Out
9/20	6 - Fully-connected NNs, Optimization, Initialization	Kolter	pdf
9/22	7 - Normalization, Regularization, Dropout	Kolter	pdf
9/27	8 - NN Library Implementation 1	Chen	pdf
9/29	9 - NN Library Implementation 2	Chen	ipynb	HW1 Due
10/4	10 - Convolutional Networks	Kolter	pdf	HW2 Out
10/6	11 - Hardware Acceleration for Linear Algebra	Chen	pdf
10/11	12 - Hardware Acceleration + GPUs	Chen	pdf
10/13	13 - Hardware Acceleration Implementation	Chen	ipynb
10/18	14 - ConvNet Implementation + GPUs	Kolter	ipynb	HW2 Due
10/20	15 - Sequence Modeling + RNNs	Kolter	pdf
10/25	17 - Training Large Models	Chen	pdf	HW3 Out (TBA)
10/27	16 - Sequence Modeling Implementation	Kolter	ipynb
11/1	18 - Architecture Overview Hardware Acceleration	Chen	ipynb
11/3	19 - Transformers + Attention	Kolter	pdf
11/8	20 - Transformers + Attention Implementation	Kolter	ipynb
11/10	21 - Generative Adversarial Networks	Chen	pdf
11/15	22 - Generative Adversarial Networks Implementation	Chen	ipynb	HW3 Due
11/17	23 - Model Deployment	Chen	pdf
11/22	24 - Machine Learning Compilation and Deployment Implementation	Chen	ipynb	HW4 out
11/29	25 - Future Directions / Q&A	Both	pdf
12/1	26 - Student project presentations	Students