קורס זה מוגש בשפה האנגלית באמצעות פלטפורמת edX ולכן דורש הרשמה נפרדת.
What you will learn
- About the fundamental ideas of sparse representation theory – exploring properties such as uniqueness, equivalence, and stability.
- About sparse coding algorithms and their proven ability to perform well.
This course introduces the fundamentals of the field of sparse representations, starting with its theoretical concepts, and systematically presenting its key achievements. We will touch on theory and numerical algorithms.
Modeling data is the way we - scientists - believe that information should be explained and handled. Indeed, models play a central role in practically every task in signal and image processing. Sparse representation theory puts forward an emerging, highly effective, and universal such model. Its core idea is the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements.
A series of theoretical problems arise in deploying this seemingly simple model to data sources, leading to fascinating new results in linear algebra, approximation theory, optimization, and machine learning. In this course you will learn of these achievements, which serve as the foundations for a revolution that took place in signal and image processing in recent years.
עצמי: למידה בזמן שמתאים לכם.
מונחה: לוח הזמנים נבנה ע"י צוות הקורס.
Received the B.Sc. and M.Sc. degrees from the Department of Electrical Engineering, Technion—Israel Institute of Technology, Haifa, Israel, in 2010 and 2015, respectively. She is currently pursuing her Ph.D. in the Department of Computer Science in the Technion. Her research interests are deep learning, inverse problems, and sparse representations.
Yaniv Romano received his B.Sc. degree from the Department of Electrical Engineering, Technion – Israel Institute of Technology, in 2012, where he is currently pursuing his Ph.D.. He received the 2015 Zeff fellowship, the 2017 Andrew and Erna Finci Viterbi fellowship, and the 2017 Irwin and Joan Jacobs fellowship. In parallel to his studies, he has been working in the industry since 2011 as an Image Processing Algorithm Developer. The super-resolution technology he invented as a researcher in Google was launched in 2017, leading to significant bandwidth savings of billions of images.
Prof. Michael Elad
Prof. Michael Elad
Michael Elad received his B.Sc., M.Sc., and D.Sc. degrees from the Department of Electrical Engineering, Technion –Israel Institute of Technology, Israel, in 1986, 1988, and 1997, respectively. Since 2003 he is a Faculty at the Computer-Science Department, Technion. Prof. Elad received numerous teaching awards, and was a recipient of the 2008 and 2015 Henri Taub Prizes for academic excellence, and the 2010 Hershel-Rich prize for innovation. Prof. Elad is a fellow of IEEE. He is serving as the Editor-in-Chief of the SIAM Journal on Imaging Sciences since January 2016.