Embedded Files
Instructor(s)
Prof. Yuanyuan Shi, yyshi@ucsd.edu
Co-Instructors (for 2026 Spring Offering):
Luke Bhan, lbhan@ucsd.edu
Yuexin Bian, yubian@ucsd.edu
Teaching Assistants
Benjie Miao, ece228tas@gmail.com, Office Hour: Wednesday 5:30-6:30pm (Location: FAH 2101)
Golnaz Salehi, ece228tas@gmail.com, Office Hour: Friday 10-11am (Location: Jacobs Hall Room 5101B)
Peixing Li, ece228tas@gmail.com, Office Hour: Monday TBD (Location: Jacobs 5101E)
Course Time and Location
Tuesday/Thursday 12:30 – 1:50pm, Catalyst Building 0125
Schedule
Schedule
Recommended Reading:
Week 1-2: An Introduction to Statistical Learning (with Applications in Python): Chapters 1-5
Week 3-5: Deep Learning, Part II: Chapters 6-11
Week 6 - 10: Papers and Discussions
Week 6 - Neural ODEs: https://arxiv.org/abs/1806.07366, https://arxiv.org/abs/2202.02435
Week 7 - Neural Operators: DeepONet: https://arxiv.org/abs/1910.03193
FNO: https://arxiv.org/abs/2010.08895
Theoretical results in neural operators: https://arxiv.org/abs/2304.13221
Geometry informed FNO: https://proceedings.neurips.cc/paper_files/paper/2023/file/70518ea42831f02afc3a2828993935ad-Paper-Conference.pdf
Week 8 - Physics-informed Neural Networks: https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125
How to actually train PINNs: https://arxiv.org/abs/2308.08468, https://arxiv.org/abs/2109.01050
Week 9:
Gaussian process
A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Page updated
Report abuse