Machine Learning is the field involved in using various algorithms that enable a machine (typically a computer) to learn from the data available, without making any assumptions about it. It includes multiple models, some simpler, others more advanced, that go beyond the statistical analysis of the data. Most of these models are black-boxes, though a few exhibit some interpretability. Yet, despite how well-defined this field is, several misconceptions about it conceal it in a veil of mystique.
First of all, machine learning is not the same as artificial intelligence (A.I.). There is an overlap, no doubt, but they are distinct fields. You can spend your whole life working in machine learning without ever using A.I. and vice versa. The overlap between the two takes the form of deep learning, the use of sophisticated artificial neural networks that are leveraged for machine learning tasks. Computer Vision is an area of application related to the overlap between machine learning and A.I.
What’s more, machine learning is not an extension of Statistics. Contrary to what many Stats fans say, machine learning is an entirely different field distinct from Statistics. There are similarities, of course, but they have fundamental differences. One of the key ones is that machine learning is data-driven, i.e., it doesn't use any mathematical model to describe the data at hand, while Statistics does just that. It's hard to imagine Statistical models without a data distribution or some function describing the mapping, while machine learning models can be heuristics-based instead.
Nevertheless, machine learning is not purely heuristics-based and, therefore, void of theoretical foundations. Even if it doesn't have the 200-year-old amalgamation of the Statistics theory, machine learning has some theoretical standing based on the few decades of research on its back. Many of its methods rely on heuristics that "just work," but it's not what people consider alchemy. Machine learning is a respectable scientific field with lots to offer both to the practitioner and the researcher.
Beyond the misconceptions mentioned earlier, there are additional ones that are worth considering. For example, machine learning is not plug-and-play, as some people think, no matter how intuitive the corresponding libraries are. What's more, machine learning is not always the best option for the problem at hand, since some projects are okay with something simple that's easy to understand and interpret. In cases like that, a statistical model would do just fine.
It's hard to do this topic justice in a single blog post, but hopefully, this has given you an idea of what machine learning is and what it isn't. I talk more about this subject in one of my most recent books, Julia for Machine Learning. Additionally, I plan to cover this topic in some depth in a 90-minute talk at the next Data Modeling Zone conference in Belgium this April. I hope to see you there! Cheers.
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.