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