ML has its roots in statistical science. Remember when you have a cloud of points on an x-y frame and try to find the straight line that best fits all of them at the same time? This is what we call a linear regression and can be solved with a simple analytical formula. Regression is the first algorithm that you typically study when getting started with ML.
To acquire perspective, be aware that, before 1980, artificial intelligence and ML were part of the same corpora of knowledge. Then, artificial intelligence researchers focused their efforts on using logical, knowledge-based approaches, and ML kept the algorithmic approach, regression being the most basic and having neural network-based algorithms as its main bundle. Hence, this fact favored that ML evolved as a separated discipline.
Following path of the traditional research in neural networks in the &apos...
