You can find useful resources for this course below
TA
Brooks(Ruijia) Niu rniu@ucsd.edu
Mya Bolds mbolds@ucsd.edu
WeiKang Wan w2wan@ucsd.edu
Victoria Zhang zhz091@ucsd.edu
Tutor:
Anthony Tong a8tong@ucsd.edu
Lulu Shao sishao@ucsd.edu
Hargen Zheng yoz018@ucsd.edu
Office Hour:
Brooks Niu | 11:30am-12:30am | Monday | CSE B275
Mya Bolds |8:30am-9:30am | Tuesday | CSE B270A
Weikang Wan |15:00pm-16:00pm | Friday | Zoom Link
Victoria | 4:00 pm - 5:00 pm | Wednesday | Zoom or CSE B250A
Anthony Tong | 1:00pm - 2:00pm | Wednesday | CSE B250A
Lulu Shao | 2:00pm - 3:00pm | Monday | CSE B250A
Hargen Zheng | 2:00pm - 3:00pm | Wednesday | CSE B250A
In this course, you will learn the fundamentals of deep learning. Part of the learning will be through in-class lectures and take-home assignments, but you will really gain hands-on experience by participating in Deep Learning Competitions.
You will participate in the deep learning competition via Kaggle in small groups, normally 1-4 people. The project contributes to 45% of the course credits:
10 % milestone report
15 % final report
10 % final presentation
10 % competition ranking
In the end, you will write a 4-page report about your project.
This competition challenges participants to develop machine learning models that can accurately emulate a physics-based climate model to project future climate patterns under varying emissions scenarios. Your models will be evaluated on their ability to capture both spatial patterns and temporal variability - key requirements for actionable climate predictions.
Note: You will have to accept the invitation link with a Kaggle account registered to your UCSD email-id first: https://www.kaggle.com/t/728b8ca3672a42a0bc7efd2297f36571
Competition Page : https://www.kaggle.com/competitions/cse-151-b-spring-2026-competition
Compute: UC San Diego provides Data Science/Machine Learning platform for this course. You can log in with your Active Directory ID.
In addition, you can apply for student cloud computing credits at Google Cloud and Amazon AWS.
Reference: Argoverse: 3D Tracking and Forecasting with Rich Maps