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DO WE NEED MATHEMATICS IN MACHINE LEARNING?
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And that is the question.
I clearly remember the time when I was pursuing a Bachelor's Degree in Education having Mathematics as the major field. I know I can cope with the basics, but it's a different thing when the subjects are advancing. That was when I realized, my weakness, numbers combined with letters and symbols. Those complex and confusing mathematical equations which almost made me quit studying during the early part of the semester.
But as I continue, I discovered that some of my classmates were not as good in application and analysis. When it comes to problems solving, I can solve and present a good solution. While they are good at solving complex mathematical equations. We joined forces and we passed the subject.
Though I wasn't able to finish the course because I need to start a new job and I have to transfer to another place. I never thought that I need to continue this struggle because of my new interest - artificial intelligence.
MATHEMATICS FOR MACHINE LEARNING
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My recent C-Blog, How to Learn Machine Learning, I mentioned about mathematics, specifically statistics as a prerequisite to studying machine learning.
We may want to study machine learning or artificial intelligence, but we are worried that our math skills may not be up to it. Or it might be that what we learned during our college days has already been forgotten.
Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization. These principles may cause would-be AI practitioners to be discouraged from continuing.
WHY MATHEMATICS IS IMPORTANT IN MACHINE LEARNING
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Mathematics is the core of everything. Undeniably, mathematics form part of our daily lives. Just close your eyes for a second and imagine the things that surround you. The date in the calendar you are looking at. The number of days in a month you need to complete in order to jump into another month. Computing your daily earnings from the site you are working with. All these and more represent mathematics in one way or another.
Here are some of the reasons why mathematics plays a vital role in machine learning.
>We need to consider accuracy, training time, model complexity, number of parameters and number of features, thus we need to choose the right algorithm for a certain project.
>We need to choose the appropriate parameter settings and validation strategies.
>Understanding the Bias-Variance tradeoff is necessary when identifying underfitting and overfitting.
>We need to estimate the right confidence interval and uncertainty
All these and other actions clearly show that mathematics is essential in machine learning.
MATHEMATICS AND ITS ROLE IN MACHINE LEARNING
There are two main fields of mathematics, Pure Mathematics, and Applied Mathematics. Pure Mathematics includes Algebra, Calculus and analysis, Geometry and topology, Combinatorics, Logic and Number theory. While Applied Mathematics include Dynamical systems and differential equations, Mathematical physics, Computation, Information theory and signal processing, Probability and Statistics, Game theory, and Operations research.
Image Credits: Querlo Screenshot of Artificial Intelligence, Mathematics and Its Role in Machine Learning
We don't need to learn all of these things in order to start studying machine learning. In this Querlo C-Blog, I will share with you the role of each field of mathematics we need in machine learning. Please interact with me in this C-Blog:
CLICK HERE FOR A FULL-SCREEN VIEW
On the final thought:
There are certain things in this world that require a set of more advanced knowledge and skills before we could pursue. Oftentimes, these prerequisites appear to be too complex for us to handle and would most of the time discourage us from going further. However, if we won't give it a try, we won't be able to know the result. Sometimes, we only need to give a little more effort.
Math is the language of the universe. So the more equations you know, the more you can converse with the cosmos.
Neil deGrasse Tyson
Thank you for reading. I hope you learned something worthwhile from this blog and I hope to see you again in my next post. Have a great day!
You may also find the following interesting:
ARTIFICIAL INTELLIGENCE: Why Do We Need to Learn Python
ARTIFICIAL INTELLIGENCE: How to Learn Machine Learning
Artificial Intelligence: Everything We Need to Know About Querlo
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DISCLAIMER: The views and opinions expressed in this c-blog post are that of the author and does not in any way represent the agency or department she currently belongs.
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Written for Bitlanders
by ★Sharon Lopez
Date: August 26, 2019
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