DEEP Learning

Class overview

This course covers the fundamentals of deep neural networks at the undergraduate level. We introduce linear regression, multi-layer perceptrons, back-propagation, and automatic differentiation. We will also discuss Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. The course will be a combination of lectures, presentations, and machine learning competitions.


Week 1 (April 3rd)                                     Introduction and Background                                             HW 1 release

Week 2 (Apr 10th)                                     Multi-layer perceptron                                                       

Week 3  (Apr 17th)                                    Automatic Differentiation                                                    HW 2 release

Week 4  (Apr 24th)                                   Convolutional neural network

Week 5  (May 1st)                                     Recurrent neural network                                                   HW3 release

    Week 6 (May 8th)                                      Mid-term week 

    Week 7 (May 15th)                                    Deep learning implementation                                         HW4 release

    Week 8 (May 22nd)                                   Attention and Transformer                                           Milestone report due

    Week 9 (May 29th)                                    Graph neural network                  

    Week 10 (June 5 th)                                   Presentation week                                                           Final report due


Class Assessment



Q: What are the pre-requisites?

Q: Can first year undergraduates take this course?


About me

My Chinese name is Qi Yu. That is also the instructor's name in the registrar's office.  I publish under the name Rose Yu. You can learn more about my research at my personal website.