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Ilona Codes
Ilona Codes

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Reasons to Learn Probability For Machine Learning

As science and engineering move forward, we end up dealing with more complex systems. And in a complex system, we cannot expect to have a perfect model of each component or to know the exact state of every piece of the system. So uncertainty is now the foreground and needs to be modeled.

We also live in an information society. Data and information play an increasingly central role, both in our individual lives and in the economy as a whole. Now, data is only useful because it can tell us something we did not know. The reason to leverage information is to reduce uncertainty by understanding its nature.

And this is why probability theory and its children — statistics and inference — are a must.

"Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events."

Because everything involves uncertainty and calls for probabilistic models.

Quantum mechanics, biological revolution, markets, transportation systems, customer demand are random. And the only reason to collect and manipulate data is to fight this randomness as much as possible. And the first step is to study and understand randomness.

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Photo by Balázs Utasi from Pexels

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