|Instructor:||Deva Ramanan (firstname.lastname@example.org)|
|Office hours:||W||12:30-2:00pm||DBH||4072||(or by appointment)|
Image understanding, extracting useful semantic content from image data, is a core human competency whose emulation by machine systems has been an area of active research in artificial intelligence for the last 40 years. Contemporary computer vision research draws heavily from machine learning and serves as a testing ground for new ML theories and algorithms. Computer vision in turn provides a set of tools for many applications in multimedia information systems and HCI, as well as the natural sciences, e.g. biomedical imaging. Graduate students completing this course will be well prepared to comprehend current research in computer vision or apply state-of-the-art techniques to problems of interest in their own field.
The following undergraduate courses or their equivalent: ICS 6D/Mathematics 6D, Mathematics 6G or 3A, Mathematics 2A-B, ICS 23.
Required course materials
Recommended course materials
4 homeworks (with MATLAB implementation) worth 15% each and a class project worth 40%.
Homeworks can be discussed, but each student must independently write up their own solutions. In particular, no sharing of code. Please see the university policy on academic honesty. It is fine to use reference materials found online, but do not search for homework solutions. Rather, students are strongly encouraged to ask questions at both office hours and on the class discussion group.
I gladly acknowledge a host of other instructors for making their teaching materials available online. A special thanks to Antonio Torralba for the nifty picture above.