What is Machine Learning: An Introduction for Beginners

G.R. Liu

Author

Dr. G.R. (Guirong) Liu is an expert in computational mechanics, particularly known for pioneering work in meshfree methods, smoothed finite element methods (S-FEM), and particle-based simulations. He currently serves as a Professor of Aerospace Engineering and Engineering Mechanics at the University of Cincinnati. A prolific researcher and educator, Dr. Liu is recognized for pioneering meshfree methods, smoothed finite element methods (S-FEM), and smoothed particle hydrodynamics (SPH). These innovations have significantly advanced simulation techniques in both solid and fluid mechanics. In recent years, Dr. Liu has authored several textbooks and reference works for courses and research in areas such as artificial intelligence, machine learning, mathematics, computational methods, mechanics of materials, solid mechanics, engineering mechanics, applied mechanics, and fluid dynamics.

Synopsis

Machine learning is real, it is everywhere, and we interact with it every day—often without noticing. Whenever we use a smart phone, for example, we are almost certainly relying on ML models running behind the scenes. With rapid advances in both computer hardware and software, a broad range of machine learning methods, techniques, algorithms, and models have emerged, supporting applications across many fields. These methods are sophisticated and interconnected in practice: a single technique can be applied to multiple domains, and the same problem can often be approached using different ML methods. In this booklet, we explore what machine learning is, how it works, and why it matters from various perspectives. The discussion is intentionally kept at a high level, without diving too deeply into technical or mathematical details. As a principle, we avoid the use of equations.