<story><title>An introduction to machine learning through polynomial regression</title><url>https://rickwierenga.com/blog/machine%20learning/polynomial-regression.html</url></story><parent_chain><item><author>geoalchimista</author><text>I&amp;#x27;m really not so amused by the bloating of words like &amp;quot;machine learning through polynomial regression&amp;quot;. People have been doing polynomial regression for centuries (almost as old as linear regression) and how does it become a new thing under the umbrella of machine learning? And not to mention, normal equation is vastly superior to gradient descent in solving out the coefficients for polynomial regression. (The criticism is not directed to OP but to the field in general.)</text></item></parent_chain><comment><author>rickdeveloper</author><text>As others have mentioned I did not intend to bloat by using the word &amp;#x27;machine learning.&amp;#x27; Polynomial regression is taught as first concept in many machine learning courses so I figured it might be a good way to introduce newcomers to the field. Many of the posts currently available only show how to make another neural network for MNIST, but do not cover the background theory. I know that polynomial regression is a concept of statistics, but I would argue most of machine learning is too. That is the reason I choose this concept as an intro to machine learning (and consequently statistics).&lt;p&gt;Thank you for your feedback. I really appreciate it. I have added a paragraph on the normal equation (should be online anytime) and why you might&amp;#x2F;might not want to use it.</text></comment>
<story><title>An introduction to machine learning through polynomial regression</title><url>https://rickwierenga.com/blog/machine%20learning/polynomial-regression.html</url></story><parent_chain><item><author>geoalchimista</author><text>I&amp;#x27;m really not so amused by the bloating of words like &amp;quot;machine learning through polynomial regression&amp;quot;. People have been doing polynomial regression for centuries (almost as old as linear regression) and how does it become a new thing under the umbrella of machine learning? And not to mention, normal equation is vastly superior to gradient descent in solving out the coefficients for polynomial regression. (The criticism is not directed to OP but to the field in general.)</text></item></parent_chain><comment><author>curiousgal</author><text>&amp;quot;Machine Learning&amp;quot; these days is people rediscovering statistics.&lt;p&gt;It&amp;#x27;s always amusing how stats are underappreciated. I was at a career fair the other day talking to a manager at HSBC, he was surprised to know that a stats curriculum includes stochastic calculus...</text></comment>