This is the first of two educational seminars overviewing physics-guided machine learning. In the past few decades, machine learning has emerged as a fundamental and integral approach to conduct scientific work besides experiment, theory, and computation. However, in computational sciences and applied mathematics, it is not uncommon to get trapped in “black box” implementation. Besides losing interpretability, such implementations can easily lead to computational artifacts and wrong conclusions. This motivated many to call for “physics-guided” or “physics-informed” ML implementations. In this first seminar, the essential elements of “physics-guided” machine learning are presented, analyzed, and illustrated in some real-life examples.