Information conveyed in real-world signals may be often affected by noise, partially corrupted or even unavailable. Thus, extracting desired information from real-world signals can be even very complicated. Machine learning for signal processing (MLSP) is the science that deals with the development of efficient algorithms and models that are able to detect and unveil a possible hidden structure in signals, thus recovering a desired information. This process is autonomously and automatically performed by MLSP algorithms, by simply learning from the available data, which is the basis of any science related to artificial intelligence.