You can find useful resources for this course below
TA :
Sophia Huiwen Sun (shs066@ucsd.edu)
Abdulaziz Almuzairee (aalmuzairee@ucsd.edu)
Hanrui Fan (h8fan@ucsd.edu)
Office Hour:
Sophia |11:00 am - 12:00 pm | Monday | B240A - CSE Basement
Abdulaziz| 1:00 pm - 2:00 pm | Thurs | CSE 3109
Hanrui Fan |3:00 - 4:00 pm |Thurs |B250A - CSE Basement
Gradescope: https://www.gradescope.com/courses/1009757
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 a 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 focuses on developing accurate motion forecasting models using the Autonoumous Driving Motion Forecasting Dataset. Participants will predict future trajectories of diverse agents (vehicles, pedestrians, cyclists, etc.) in complex, real-world driving scenarios.
Please first register for the competition via the [Participation Link].
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