Machine Learning: History and Terminology

This chapter traces the evolution of machine learning (ML), from its early theoretical foundations to its contemporary applications in various scientific disciplines. Starting with Alan Turing’s seminal work and the development of the perceptron in the mid- 20th century, We explore briefly key milestones like the introduction of neural networks, the impact of Minsky and Papert’s book, and the resurgence of ML in the late 20th century with advancements in algorithms and computational power. The chapter highlights the transition from theoretical research to practical applications, marked by significant developments such as backpropagation, decision trees, support vector machines, and reinforcement learning. The role of ML in fields such as chemistry, physics, and biology is discussed, emphasising its transformative impact on drug discovery, high-energy physics, astrophysics, genomics, and proteomics. The chapter concludes by categorising ML into various types: supervised, unsupervised, semi-supervised, and reinforcement learning, each of which plays a distinct role in advancing scientific knowledge.