The mathematical and statistical foundations of Machine Learning (ML) have long roots in physics, chemistry, and biology to discover trends and inform theory. For example, linear regression, principal component analysis, and discriminant analysis were used throughout the 20 th century to understand trends in spectroscopy data . The growth of ML, as it is thought of today, would come during the mid-20 th century when researchers questioned how machines can be programmed to learn inductively from data (rather than deductively from pre-programmed rules and logic). This led to many early successes such as Marvin Minsky's "SNARC" analog neural network , Frank Rosenblatt's perceptron modeled after the neuron , Hopfield networks and Boltzmann machines , and support vector machines . In the late-20 th century, much of the theory of connectionism discussed in this review was formalized, including feed-forward and convolutional neural networks and methods of parameter initialization and training . However, it was not until the 2000s that researchers had the computational power necessary to train larger ML models and apply them at a broader scale. Primed by the influx of data concomitant with the information age and developing internet, ML saw a surge in use cases such as search engines and online behavior analytics. It was around this time that the freshly marketed field of ML saw use in materials science, increasingly cited in materials research publications starting from the early 2010s . Open-source ML software libraries like scikit-learn, PyTorch, and TensorFlow were developed to make ML more accessible, meaning ML could be more easily implemented in materials science problems. At the same time, materials data infrastructure like the Materials Project 65 made a wealth of materials data available to the wider research community, resulting in the online "materials intelligence ecosystem" we know today.