CS281B
Computer Vision and Image Analysis
Winter 2017
Lecture: MW 1:00 p.m. - 2:50 p.m. |
Room: Phelps 2510 |
Enrollment Codes: 57760 (CS), 59493 (ECE) |
Class Web: http://www.cs.ucsb.edu/~cs281b |
Instructor: Yuan-Fang Wang |
Office: HFH 3113 |
Phone: 893-3866 |
Office Hours: MW 12:00 p.m. - 1:00 p.m. |
Textbooks:
None required. The following is a partial list of graduate-level books that cover "traditional" computer vision topics that might serve as good references.
This course will have a large machine learning component, and the following books cover useful background material.
-
T. Hastie, R. Tibshirani, and J. H. Friedman, "The Elements of Statistical Learning", 2nd edition, Springer, 2009
-
Richard O. Duda, Peter E. Hart, and David G.Stork, "Pattern Classification", second edition, Wiley-Interscience, 2001
-
John Shawe-Taylor and Nello Cristianini, "Kernel Methods for Pattern Analysis", Cambridge University Press, 2004
-
Tom M. Mitchell, "Machine Learning", McGraw-Hill, 1997
Topics:
This particular incarnation of CS281b will focus heavily on
deep learning techniques for computer vision.
Topics may be added and/or deleted depending on the available
time and interest of students.
Please consult
CV Lecture Notes
and
ML, PR and ANN Lecture Notes
pages for more details
Labs:
CSIL and GSL (for CS students), ECI labs for all engineering students.
Final exam:
There will be no written final exam, but there will be a class project with presentations and reports
Prerequisites:
-
There are no specific pre-requisites for this course.
However, a certain degree of sophistication in math and programming,
commensurate with the gradaute standing in computer science and
engineering, is assumed. In particular, solid background in calculus, probability, and linear
algebra is a must.
-
Or CS/ECE 181B is not a prerequisite for this course.
-
Proficiency in at least one high-level programming
languages (e.g., Python) is assumed.
Grading Policy:
-
There is no exam (midterm or final).
-
Your grades in this class will be determined by (1)
class attendance and discussion (20%), (2) programming
assignments (40%) and (3) a class project (program + presentation) (40%).
General Class Policies:
-
This class has a Web site at
http://www.cs.ucsb.edu/~cs281b.
You will fine useful information such as announcements, on-line copies
of syllabus and class notes, and links to other interesting Web sites.
It is your responsibility to check the Web site on a regular
basis.
-
It should be noted that the specification in the syllabus can still change.
ECI staff has not yet been able to get the GPU-version of Tensorflow running in CSIL.
There might be other alternatives (e.g., Amazon web servers), but the service may not be free
and knowlege of systems/packages installation may be required. Most machine learning packages
use a Python interface, which may or may not be familiar to some students. We will have a discussion of
all these issues.
Back to the Course Home Page