Announcements:
• Welcome to CS131!• Schedule information may change during the quarter; please visit the Syllabus page regularly to stay up to date.
• Lecture location has changed to 370-370 due to the high volume of student enrollment.
Course Instructor:
Office: Room 246, Gates Building
Office hours: Tuesday, 3-4pm
Office: Room 243, Gates Building
Office hours: By Appointment
Class forum on Piazza (please ask all questions here if possible): piazza.com/stanford/fall2016/cs131
Course Team Email (for privacy-sensitive questions): cs131-fall1617-staff@lists.stanford.edu
Course Assistants:
Office hours: Thursday, 10am-12pm, Huang Basement
Office hours: Tuesday and Thursday, 12-1pm, Huang Basement
Office hours: Tuesday, 9:30-11:30am, Huang Basement
Office hours: Wednesday, 12:45-2:45PM, Huang Basement
For questions outside office hours, please use the class forum:
piazza.com/stanford/fall2016/cs131
Class Time and Location:
Lectures: Tuesdays and Thursdays 1:30pm to 2:50pm in 370-370
We may have a few sessions
at irregular times; see the Syllabus. Course Description: What do the following technologies
have in common: robots that can navigate space and perform duties,
search engines that can index billions of images and videos, algorithms
that can diagnose medical images for diseases, or smart cars that can
see and drive safely? Lying in the heart of these modern AI
applications are computer vision technologies that can perceive,
understand and reconstruct the complex visual world. Computer Vision is
one of the fastest growing and most exciting AI disciplines in today’s
academia and industry. This course is designed to open the doors for
students who are interested in learning about the fundamental
principles and important applications of computer vision. During the
10-week course, we will introduce a number of fundamental concepts in
computer vision. We will expose students to a number of real-world
applications that are important to our daily lives. More importantly,
we will guide students through a series of well designed projects such
that they will get to implement a few interesting and cutting-edge
computer vision algorithms. Grading Policies: Homework: 80% Final exam: 20% Extra credit: 2% for students who participate actively on piazza Turning in assignments We strongly recommend using LaTex, but
also accept other typed or scanned assignment. However, students must be responsible for the legibility and
we reserve the right to deduct points if the solution is not clear.
Here is the template for Latex.
All
assignments (with code attached) must be turned in to:
GradeScope. Make an account and sign up
for the class using the code: MBRJEM.
All
code must also be submitted via email to
cs131.submissions@gmail.com
as a zip file "yourSUNetID_HW[0-5]_code.zip".
No
paper submission is required for HWs.
Prerequisites
We
hope that you are familiar with:
• HW0 (theoretical + programming): 8%
• HW1 (theoretical): 13%
• HW2 (programming and writeup): 13%
• HW3 (theoretical): 13%
• HW4 (programming and writeup): 13%
• HW5 (theoretical + programming): 20%
Using Late Days:
• You have 5 free late days total.
• You can use up to 3 late days per assignment. (Homework
will not be accepted more than 3 days late.)
• Please put number of late days used in the first page of your pdf.
• If you have used all of your late days, there is a 25%
penalty for each day late.
•
Explicitly mark the number of late days you use on an assignment if you
are using late days. For example, if you turn it in by 5pm the next
day, write "1 late day." If it's 5:01 pm the next day, write "2 late
days." It is an honor code violation to write down the wrong time. (If
you turn in late and don't write the number of days, we'll round up to
3.)
• College-level calculus (e.g. MATH 19 or 41) - You’ll need
to be able to take a
derivative, and maximize a function by finding where the derivative=0.
• Linear algebra (e.g. MATH 51) - We will use matrix
transpose, inverse, and
other operations to do algebra with matrix expressions. We’ll use
transformation matrices to rotate/transform points, and we’ll use
Singular Value Decomposition. (These topics are important for the
homeworks, but if you are a quick learner you should be able to learn
them during the class if you haven’t yet. We will have review sessions
and provide review materials.)
• Basic
probability and statistics (e.g. CS 109 or other stats course) - You
should understand conditional probability, mean, and variance.
• We
also require a decent amount of programming skills, such as entry-level
Matlab, and the ability to work in the Linux environment. If you are
unsure about your background, we encourage you to try out Problem Set
0, which is a “normalizing” problem set for the class. HW0 will help
you gauge if CS131 is the right level for you.