(Header image courtesy of Andrej Karpathy’s blog)
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, as well as high-level vision such as scene understanding using neural networks.
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
ELEC 301 OR ELEC 475 OR COMP 330
Students are expected to be able to program in Python. It is also recommended that students have a basic understanding of machine learning and deep neural networks. While the instructors will try to make the class self-contained, students are responsible for filling in any gaps in their knowledge on their own, such as with the additional content in Course Materials or elsewhere. Also, TAs will be hosting a ‘Basics of Python and Colab’ session (check Schedule).
Grading
Homework Assignments (5): 60%
Midterm: 15/25%
Final: 15/25%
Participation (In class and Piazza): Up to 5% bonus
Final Exam Room and Time
Keck 100, April 29, 2-3:30 PM
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
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| Kushal Vyas | Krish Kabra | Shiyu Tan | Zishen Li |
Lectures
MW 4:00-5:15, KCK 100
Office Hours
Professor Balakrishnan:
Thursdays, 1-2 PM, Zoom
Kushal & Krish:
Tuesday 4-5pm, Hybrid: Zoom and Duncan Hall 2014
Zishen & Shiyu:
Fridays, 3-4 PM, Zoom
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.