In recent years it has become very easy to collect data thanks to many devices such as phones, cars and buildings that have sensors. All these produce raw data, which require pre-processing operations in order to be used. Signal processing is designed to achieve this purpose
Graph signal processing is a special area in signal processing based on spectral graph theory that allows to synthesis the manifold structure of the data. It can be then exploited for many machine learning or signal-processing tasks including: filtering, de-noising, in-painting, compression, clustering, partitioning, sparsification, features extraction, classification and regression.
Optimization is a field of mathematics where the goal is to select the best element with respect to criteria from a set of available alternatives. Put this way, it resembles a mathematician’s playground. However, optimization is at the heart of many algorithms. It is used to answer direct questions like: What is the best financial investment that I can make? What is the best shape for this component? Where is the safest place to live?
Machine learning is the science of getting computers to perform a given task “without being explicitly programmed” (Arthur Samuel in 1959). In the two last decades, machine learning has imposed itself in our every-day life with self-driving cars, speech or face recognition, effective web search, spam detection and product recommendation systems. Machine learning is so ubiquitous today that you probably use it multiple times in a day without actually realizing it. Some researchers even believe that it will lead us towards human-level artificial intelligence.