UCSD ECE 285: Introduction to Visual Learning (Spring 2024)

Time and Location

• Lectures: Tuesday and Thursday, 2:00 pm - 3:20 pm, Ledden Auditorium.

• Office Hours:

  • Monday, 10:00 am - 11:00 am, Franklin Antonio Hall, 3rd floor open space.

  • Friday, 4:00 pm - 5:00 pm, Franklin Antonio Hall, 3rd floor open space.

Instructors

• Xiaolong Wang: xiw012@ucsd.edu

• Isabella Liu: lal005@ucsd.edu

• Jiteng Mu: jmu@ucsd.edu

• Nicklas Hansen: nihansen@ucsd.edu

Overview

This course will cover the fundamentals and recent developments in computer vision. The course is composed of two parts: The first part is going to cover basic techniques for image and video understanding with deep learning; The second part is going to introduce the latest progress on computer vision and research directions. We will not only introduce the techniques with lectures but also provide hands-on implementation courses in PyTorch.

Requirements

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.

Grading

Grading will be based on 4 programming homework assignments, 1 final project proposal report and 1 final project. The programming assignments include programming in python and a submitting report along with the code. You can team up for the final project, each team has 1 or 2 students. For the 2-people team, each student should explicitly state what did they do in the report. Each student needs to do some coding for the final project, cannot just “do experiments”, “write reports”, “contributed ideas”. The proposal has to be a minimum of one page long. The final project proposals are due by May 2, 2024.

Details of Final Project can be found here.

References

• Introduction to Deep Learning by Svetlana Lazebnik

• Convolutional Neural Networks for Visual Recognition

• Deep Learning

• Computer Vision

Collaboration and Academic Integrity

Please 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 Center

Please 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.