Welcome! This course covers fundamental to advanced concepts in computer vision. This includes topics in early to mid-level vision such as signal processing and feature extraction, to high-level vision such as scene understanding using neural networks and foundation models.
Piazza and Canvas
We will be using Piazza to answer any questions and engage in discussions outside of lecture. Please enroll using this link. Assignments will be submitted through Canvas. The instructor will also send announcements to the entire class through Canvas.
Prerequisites
Students are expected to be able to program in Python and have a basic understanding of machine learning and deep neural networks. While the instructor will try to make the class self-contained, students are responsible for filling in any prerequisite knowledge gaps on their own.
Lectures
MW 4:00-5:15, HRZ 212
Grading
Homework Assignments (5/6): 60%
Midterm & Final Exams: 40%
Participation: Those who participate regularly will gain a benefit on final grades at the borderline between grade levels.
Final Exam Room and Time
TBD
ELEC/COMP 546
The graduate version of this class (546) will use the same homework assignments and exam as 447, but with an extra question or two per assignment.
Instructor
Professor Guha Balakrishnan (guha@rice.edu)
Teaching Assistants (TAs)
Yumin Su
Hao Liang
Abhirami Kathirvel
Jayanth Mouli
TA Office Hours
Abhirami and Hao: Monday @ 2 pm, zoom link
Yumin and Jayanth: Thursdays @ 1 pm, zoom link
Please go to Piazza first if you have questions before coming to office hours! It is possible one of your classmates asked the same question already. If they did not, then post your question to help your classmates. The instructors will check and answer questions on Piazza as fast as possible.