## UCSD ECE176: Introduction to Deep Learning & Applications (Winter 2023)## Time and Location**Lectures**: Tuesday and Thursday, 3:30 pm - 4:50 pm, WLH 2005.**Office Hours**:Thursday, 10:30 - 11:30 am, on Zoom (see canvas for link). Friday, 2:00 - 3:00 pm, in FAH 3301.
## InstructorsXiaolong Wang: xiw012@ucsd.edu Ruihan Yang: ruy002@eng.ucsd.edu Yuzhe Qin: y1qin@eng.ucsd.edu
## OverviewThis course covers the fundamentals in deep learning, basics in deep neural network including different network architectures (e.g., ConvNet, RNN), and the optimization algorithms for training these networks. We will have hands-on implementation course and assignments in python and PyTorch. This course will introduce the deep learning applications mostly in computer vision, and will also cover applications in robotics and sequence modeling. ## PrerequisitesStudents are expected to have basic background on Linear Algebra and programing skills with python. Students should have taken the following courses or courses related to them: MATH 18: Linear Algebra (4) ECE 143: Programming for Data Analysis MATH 31AH: Honors Linear Algebra
## RequirementsThe course will be taught in person (WLH 2005) and synced with zoom. ## OverviewThis course covers the fundamentals in deep learning, basics in deep neural network including different network architectures (e.g., ConvNet, RNN), and the optimization algorithms for training these networks. We will have hands-on implementation course and assignments in python and PyTorch. This course will introduce the deep learning applications mostly in computer vision, and will also cover applications in robotics and sequence modeling. ## PrerequisitesStudents are expected to have basic background on Linear Algebra and programing skills with python. Students should have taken the following courses or courses related to them: MATH 18: Linear Algebra (4) ECE 143: Programming for Data Analysis MATH 31AH: Honors Linear Algebra
## RequirementsThe course will be taught in person (WLH 2005) and synced with zoom. The course will be recorded and the recorded lecture will be uploaded to Canvas. Students are expected to sign up on Piazza and GradeScope. Discussion and important announcements will be made on Piazza. The homework should be turned in and will be graded on GradeScope. ## GradingGrading will be based on 4 programming homework assignments, 1 final project proposal report and 1 final
project. The programming assignments include programming in
## References## Collaboration and Academic IntegrityPlease note that an important element of academic integrity is fully and correctly attributing any materials taken from the work of others. You are encouraged to work with other students and to discuss the assignments in general terms (e.g., “Do you understand the A* algorithm” or “What is the update equation for Value iteration?”). However, the work you turn in should be your own – you should not split parts of the assignments with other students and you should certainly not copy other students’ code or papers. All projects in this course are individual assignments. More generally, please familiarize yourself with UCSD's Code of Academic Integrity, which applies to this course. Instances of academic dishonesty will be referred to the Office of Student Conduct for adjudication. ## IDEA Engineering Student CenterPlease consider participating in the programs and events organized by the IDEA Engineering Student Center. The IDEA center, located just to the right of the lobby of Jacobs Hall, is a hub for student engagement, academic enrichment, personal and professional development, leadership, community involvement, and a respectful learning environment for all. The IDEA center's mission is to foster an inclusive and welcoming community, promote academic success, develop engineering leaders, and, most importantly, support your mental health and wellness needs. These opportunities can be found on the IDEA Center Facebook page and the Center web site. |