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Machine Learning

Although often confused with Artificial Intelligence (AI) by the general public, Machine Learning (ML) is a branch of it. Unlike a traditional AI algorithm, which can relies on hard-coded rules (such as many if and else conditions), ML learns from data to provide more appropriate responses. Historically, this learning was closely related to statistics. However, with the rise of "black box" algorithms, which may deliver better performance, a distinction has emerged between two subfields of ML: Statistical Learning (SL) and Deep Learning (DL).

In this journey through the mental palace, we will delve deeply into both SL and DL. Before we do, as scientific researchers, it is crucial to begin with an introductory chapter on statistical modeling because ML rely on fundamental statistical concepts, and a solid understanding of these foundations is essential to grasp the nuances of modern techniques.

In addition, many ML algorithms rely on optimization algorithms, so feel free to consult the corresponding chapter.