From Netflix recommendations to smart speakers, most platforms now use Machine Learning concepts. It combines many disciplines like statistics, mathematics, algorithms, and probability – one of which is empirical probability.

If you are wondering what is empirical probability and what is its place in Machine Learning, here are some essentials to understand:

What is empirical probability?

Probability theory deals with the analysis of random events. Each outcome is called a trial. The empirical probability goes a bit further and determines how many times an event occurred divided by the number of trials.

Empirical probability is used in Machine Learning, where computers learn patterns without you specifically teaching them.

In the above examples, platforms like YouTube, Google, Facebook, etc., collect your data. They combine the data as much as possible, about the kind of music or videos you like or the posts you react to. Finally, they make educated guesses about what you might want to see next.

Machine Learning makes a note of all the noises that emerge from your mouth in the case of voice assistants. That’s how they can capture subtle nuances in the accents and slang.

How Does Empirical Probability Work in Machine Learning?

Empirical probability finds its way into complex Machine Learning technology in various ways. For example, in the Empirical Bayes Methods, data collected is updated based on a prior probability. Before the creation of a posterior probability distribution, it follows this basic format:
1. Creation of hyperparameters instead of fixed values given prior assumption. (Probability distribution of what you previously watched)
2. Testing of the prior probability, turning the probability distribution into approximate values for the parameters. (On a sample data set, i.e., your recent browsing habits)
3. Get updated prior assumption as a prior probability and run it on the full data set. (Expand the trial to the full data set)

Machine Learning has three flavors:
● Supervised
● Unsupervised
● Reinforcement
The most prevalent form is supervised learning, where the data gets labeled, and the machine looks for exact patterns. This can be seen in the above-mentioned Netflix example, where you see similar show or movie suggestions.

Unsupervised learning has no data labels, making the machine look for various patterns. The algorithm sorts patterns among tons of different objects. Since they have less obvious applications, these techniques are very rare, for example, cybersecurity.  Reinforcement learning is like a trial-and-error format. As the latest frontier, it has a clear objective – understand the pattern and find the pattern. Users’ behaviors help find similar patterns, where it gets rewarded or penalized based on the outcome.

Basically, the system becomes its own teacher. You may know it from Google’s AlphaGo Zero, which defeated a human player at a complex game called Go.

Staying on Top of Automation Technology

Machine Learning and AI are the two things that are the future of technology. They are also demanding disciplines that require the constant need to broaden your skills. Understanding what is empirical probability and acing it is a significant step towards reaching your goal.