CS 249: Special Topics - Graph Neural Networks

Instructor: Yizhou Sun

Lecture times: T/R 10am-11:50am
Lecture location: classroom zoom


About the Course

This is a graduate-level research-oriented course offered in Winter 2021. The course aims to introduce and discuss recent advances in graph neural networks (GNNs), with the goal to design deep learning algorithms for graph data for different graph applications. The course contains lecture time by the instructor covering basics of graph neural networks, and paper reading and presentation by students covering recent GNN papers. The students are expected to conduct a team-based research project related to GNN and present the project to the whole class.

This is a collaborative course via GitHub: https://github.com/yichousun/Winter2021_CS249_GNN


Prerequisites


Grading


 Q & A

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.

Tips: Answering other students' questions will increase your participation score.

Find our class page at: piazza.com/ucla/winter2021/cs249


Academic Integrity Policy

"With its status as a world-class research institution, it is critical that the University uphold the highest standards of integrity both inside and outside the classroom. As a student and member of the UCLA community, you are expected to demonstrate integrity in all of your academic endeavors. Accordingly, when accusations of academic dishonesty occur, The Office of the Dean of Students is charged with investigating and adjudicating suspected violations. Academic dishonesty, includes, but is not limited to, cheating, fabrication, plagiarism, multiple submissions or facilitating academic misconduct."

For more information, please refer to the guidance.