Sparse Representations in Signal and Image Processing

Overview

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.

This Sparse Representations in Signal and Image Processing Professional Certificate from EdX in partnership with IsraelX will introduce you to the field of sparse representations, starting with its theoretical foundations, and systematically present its key achievements. We will touch on theory, numerical algorithms, and applications in image processing.

The tools and algorithms presented in this program are state-of-the art in these fields, and relevant to many applications and needs, covering signal/image restoration, segmentation, sampling, compression, recognition, anomaly detection, separation, and more. 

This program is relevant to algorithm developers in machine learning and data mining. The tools learned will help developers be more effective in revealing hidden structures of given data, which is valuable for classification, clustering, stock market prediction, and more.

Job Outlook

• Career prospects for people with similar training include algorithm developers in signal and image processing, machine learning and data mining.

• The average base salary for Data Scientists is $117K per year (Forbes).

• According to a report from Glassdoor, Data Scientists lead the pack for best jobs in America; According to the Harvard Business Review, Data Scientist is “the hot job of the decade”.

What You'll Learn

• Fundamental theoretical contributions of sparse representation theory.

• The importance of models in data processing.

• Dictionary learning algorithms and their role in using this mode.

• How to deploy sparse representations to signal and image processing tasks.