Free Online Course on Sparse Representations in Signal and Image Processing: Fundamentals

Free Online Course on Sparse Representations in Signal and Image Processing: Fundamentals

Israel X is proud to announce you a free online course named as Sparse Representations in Signal and Image Processing: Fundamentals. Learn about the field of sparse representations by understanding its fundamental theoretical and algorithmic foundations. 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. This course will start on October 25, 2017.

Course Summary

  • Duration: 5 weeks
  • Commitment: 5-6 hours per week
  • Subject: Data Analysis & Statistics
  • Institution:  Israel X
  • Languages: English
  • Price: Free
  • Session: Starts on October 25, 2017
  • Requirement: Knowledge of linear algebra and optimization
  • Certificate Available: Yes

Who Developed the Course

  • About University: IsraelX is a national consortium of higher education institutions in Israel, led by the Council for Higher Education and the Ministry of Social Equality. Israel is the international arm of Campus – the Israeli National Project for Digital Learning, whose goal is to promote general, academic and professional education in Israel in order to reduce social gaps and accelerate economic growth.
  • Mission of University: Israel’s Mission represents the State of Israel, its citizens and the Jewish people on the global stage of the United Nations. For over sixty-six years, Israel’s delegation has worked to promote international peace, prosperity, and security through UN institution

Target Audience

Advanced knowledge of linear algebra and optimization; basic familiarity with signal and image processing.

Where Could This Lead You

  • Importance of the Subject in Today’s Scenario: 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.
  • Your Career Option: If you’re wondering what your future could look like in this area, here are some potential careers are :
  1. Signal Processing and Radio Engineer
  2. Software R&D Engineer
  3. Communication / Signal Processing Algorithm Engineer

Get Extra Benefits

You can pursue a verified certificate to highlight the knowledge and skills you gain ($99). Add the certificate to your CV or resume, or post it directly on LinkedIn. Receive an instructor-signed certificate with the institution’s logo to verify your achievement and increase your job.

How to Join This Course

You should register yourself through the given link of join this free online course:

Course Format

This program is composed of two separate parts, each of which is an independent five-weeks course:

  • Part 1: Introduction to the Fundamentals of Sparse Representations.
  • Part 2: Sparse Representations – From Theory to Practice.

While they recommend taking both courses, each of them can be taken independently of the other. The duration of each course is five weeks, and each part includes: (i) knowledge-check questions and discussions, (ii) series of quizzes, and (iii) 3 Matlab programming projects. Each course will be graded separately, using the average grades of the questions/discussions [K] quizzes [Q], and projects [P], by Final-Grade = 0.1K + 0.5Q + 0.4P.

Learning Outcomes

At the end of this course, you’ll be able to:

  • 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.

 Who Will You Learn With

  • 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.
  • Yaniv Romano: 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.

Suggested Reading

You can refer this book: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing by –  Michael Elad


  • Importance of Course: At the end of the 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.
  • Importance of Certificate: By the Certificate of Achievement you will be able to prove your success when applying for jobs or courses. You can display it on your LinkedIn or CV.

Detailed Information

For more information about the course, you may visit the given link:!

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