CS180/280A: Intro to Computer Vision and Computational Photography

Course Information

Instructor: Angjoo Kanazawa, Alexei (Alyosha) Efros
GSI: Justin Kerr, Konpat Preechakul, Chung Min Kim, Brent Yi
Tutors: Jameson Crate, Jingfeng Yang, Natalie Wei, Jorge Diaz Chao
University Units: 4
Semester: Fall 2025
Gradescope Entry Code: VWX283
Ed: URL | access code
Syllabus: PDF
Location: Haas Faculty Wing F295
Time: Tues Thurs 12:30 - 1:59PM

Office Hours

Instructors: Angjoo & Alyosha (Tues/Thurs after lecture, 30 mins)
Mon: Konpat 10-11AM (BWW 1216), Brent 11-12PM (BWW 1216), Justin 11-12PM (BWW 1216), Jorge 12-1PM (BWW 1216)
Tues: Jingfeng 4-5PM (Wheeler 202), Chung Min 5-6PM (Soda 320), Jameson 6-7PM (Cory 521)
Wed: Natalie 3-4PM (Wheeler 120)

Prerequisites

This is a heavily project-oriented class, therefore good programming proficiency (at least CS 61B) is absolutely essential and required. Moreover, working knowledge of linear algebra (MATH 54, MATH 56, MATH 110, or EECS 16A) and multivariate calculus (e.g. MATH 53) are vital. Experience with machine learning and neural networks is required in the second part of the course. You must have taken beforehand or are currently taking (CS 182 or CS 189). Due to the open-endedness of this course, creativity is a class requirement.

Discussions

Discussions are GSI-led worksheets designed to help you better understand the concepts in class and in the projects. Attendance optional, but encouraged. Sheets and solutions will be posted after each week.

Topics

Course Description

The aim of this advanced undergraduate course is to introduce students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical (e.g. Gaussian and Laplacian Pyramids) to contemporary (e.g. ConvNets, GANs), with an emphasis on using these techniques to build practical systems. This hands-on emphasis will be reflected in the programming projects, in which students will have the opportunity to acquire their own images and develop, largely from scratch, the image analysis and synthesis tools for solving applications.

Exams

• Midterm: October 23 in class
• Final: December 19 location/time TBD

Note we reserve the right to shift the midterm based on content progression.

Textbook

• Foundations of Computer Vision by Torralba, Isola, Freeman
• 2nd edition of Computer Vision textbook by Rick Szeliski
• If you need a refresher of linear algebra, please see Gilbert Strang's online class.

Class Notes

The instructor is extremely grateful to a large number of researchers for making their slides available for use in this course. Steve Seitz and Rick Szeliski have been particularly kind in letting me use their wonderful lecture notes. In addition, I would like to thank Paul Debevec, Stephen Palmer , Paul Heckbert, David Forsyth, Steve Marschner and others, as noted in the slides. The instructor gladly gives permission to use and modify any of the slides for academic and research purposes. However, please do also acknowledge the original sources where appropriate.