Control theory—also known as cybernetics, a term first introduced by Ampère and later popularized by Norbert Wiener—deals with the science of regulation and communication in animals and machines. Its roots can be traced back to antiquity, inspired by the human aspiration to design mechanisms capable of performing tasks autonomously, thereby extending both freedom and efficiency.
The goals of control systems resonate closely with those of modern Artificial Intelligence (AI), highlighting not only the deep unity of Mathematics but also its remarkable power to model natural phenomena and to shape technological innovation.
In this lecture, we will trace the historical development of control theory, emphasize some of its fundamental mathematical principles, and reveal its connections with today’s machine learning paradigms. We will also showcase key applications and success stories that illustrate the transformative impact of this interplay between control and learning.
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