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.
This course aims at presenting the main machine learning paradigms and applying them for the processing of a variety of signals, including audio and speech, images, movies, music, biological, electrical and mechanical, among many others. The course is based on regular classroom lessons, which also include regular exercises in Python on practical problems.