You can find useful resources for this course below
TA
Salva Ruhling Cachay (sruhlingcachay@ucsd.edu)
Nishant Rajadhyaksha (nrajadhyaksha@ucsd.edu)
Tutor:
Anthony Tong (a8tong@ucsd.edu)
Ali Alabiad (aalabiad@ucsd.edu)
Vedant Mohan (vemohan@ucsd.edu)
Divyam Sengar (dsengar@ucsd.edu)
Charlie Sun (p3sun@ucsd.edu)
Office Hour:
Salva| Wed 2:30 - 3:30 pm| B250A
Nishant |Fri 11:30 am - 12:30 pm |B275
Anthony | Tue 3:00 - 4:00 pm | B250A
Ali | Wed 4:00 - 5:00 pm | B260A
Vedant | Mon 4:00pm - 5:00pm | B240A
Charlie | Fri 10:00 - 11:00am | B240A
Divyam | Fri 5:00 - 6:00pm | Remote: https://ucsd.zoom.us/j/99957524112
Gradescope: https://www.gradescope.com/courses/1009756
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 40% of the course credits:
10 % milestone report
10 % final report
10 % final presentation
10 % team 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/6f53c429d53099dc7cc590f9bf390b10
Competition Page :- https://www.kaggle.com/competitions/cse151b-spring2025-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