Stanford University CS231b: The Cutting Edge of Computer Vision

Announcements:

• Welcome to CS231B: The Cutting Edge of Computer Vision.

• Please enroll in "CS 231B" on Piazza.

Instructor: Prof. Fei-Fei Li

Office: Gates 246

Email: feifeili [at] cs [dot] stanford [dot] edu

Office hours: Wed 3:30-4:40, Gates 246

Instructor: Dr. Alexandre Alahi

Office: Gates 240

Email: alahi [at] stanford [dot] edu

Office hours: Tues 10:15-12:15, Gates 240

Teaching Assistant: Jonathan Krause

Office: Gates 242

Email: jkrause [at] cs [dot] stanford [dot] edu

Office hours: Mon 3:30-5:30, Wed 4:30-5:30, Gates 259

Teaching Assistant: Vignesh Ramanathan

Office: Gates 240

Email: vigneshr [at] stanford [dot] edu

Office hours: Fri 1:30-2:30 pm, Gates 240

Contact:

To contact any of the TAs or instructors, please email cs231b-spr1415-staff [at] lists [dot] stanford [dot] edu

Class Time and Location:

Monday/Wednesday, 2:15-3:30 pm, Braun Lecture hall, Mudd Building

Course Description:

More than one-third of the brain is engaged in visual processing, the most sophisticated human sensory system. In recent years, visual recognition technology has fundamentally influenced our lives. Computer vision has grown to become a core technology of several major companies, including Google, Microsoft, and Facebook.

This course is designed for those students who are interested in cutting edge computer vision research or are aspiring to be an entrepreneur using vision technology. During the 10-week course, we will guide the students through the design and implementation of three core vision technologies on three highly practical, real-world problems. We will focus on teaching fundamental theory, detailed algorithms, and practical engineering insights, and will guide students toward developing state-of-the-art systems evaluated based on the most modern and standard benchmark datasets.

Grading policy:

  Paper presentation and participation: 15%
  Course projects (including code, write up, presentation): 85%
     -Project 1: 25%
     -Project 2: 25%
     -Project 3: 35%

Late Days:

We allow a total of seven late days in total for all three projects. After using up these days, projects turned in late wil be penalized 20% per late day.

Prerequisites:

CS231A or equivalent (need instructor's approval), and a good machine learning background (e.g. CS221, CS228, CS229).
Coding skills: fluent in Matlab, C/C++.

Textbook:

None required.