Core principle of Machine Learning 

There of course are many, but for someone coming from computer science, and, software engineering, where the environment is relatively clean and certain (deterministic), it usually is a leap to understand that Machine Learning (and other elements of #AI) are not. 

Machine learning, is based on probability theory and deals with stochastic (non-deterministic) elements all the time. Nearly all activities in machine learning, require the ability to factor and more importantly, represent and reason with uncertainty. 

To that end, when designing a system, it is recommended to use a simple but uncertain (with some non-deterministic aspects)  rule, rather than a complex but certain rule. 

For example, having a simple but uncertain  rule saying “most birds fly”, is easier and more effective than a certain rule such as “Birds can fly, except flightless species, or those who are sick, or babies, etc.”

As one starts getting deeper in Machine Learning, a trip down memory lane around Probability distribution, expectation, variance, and covariance won’t hurt. 

Author: Amit Bahree

This blog is my personal blog and while it does reflect my experiences in my professional life, this is just my thoughts. Most of the entries are technical though sometimes they can vary from the wacky to even political – however that is quite rare.

Quite often, I have been asked what’s up with the “gibberish” and the funny title of the blog? Some people even going the extra step to say that, this is a virus that infected their system (ahem) well. [:D] It actually is quite simple, and if you have still not figured out then check out this link – whats in a name?

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