Trustworthy and Efficient AI

Catalog description

In this course, students read and discuss research papers about deep neural networks with a focus on not just accuracy but also resource consideration e.g., FLOPs, parameter counts, time, memory, etc. With that interest, papers about techniques to design an efficient neural network architecture, such as structured/unstructured pruning, knowledge distillation, and quantization, will be read. On top of that, other dimensional metrics of machine learning, such as trustworthiness/robustness, fairness, or privacy, will also be explored. This course includes lectures, paper readings, presentations, and discussions. Students will conduct one term project and take no exam. Students are expected to have implementation experiences on (deep) neural networks, read/present/discuss ideas from research papers, and conduct a term project and submit a term paper.