I just found out that I made a mistake in my previous post (it is fixed now). There, I argued that in order to calculate the distance between a point and a set (which has unequal variances along different dimensions), we can use the following formula:
So here we are again, with some new proposals for the distance function between a point and a set. In the previous post, we became familiar with the variance measure and two methods to map points into a new space so that the distance function in the new space behaves the same way as what intuitively seems right! Continue reading
So in our previous step, we went over some possible approaches to define a distance function between a point and a set. At the end we saw that calculating distance between the point and the average (mean) point of the set seems to be the best possible solution. I am afraid that it is not always that easy. Continue reading
I’ll start my posts with the distance function, as I believe a complete understanding of it, provides the basic idea behind most of the ML algorithms. Some of the approaches which come next are closely related to some known algorithms in ML, but I am not going to name any of them, as I believe that a complete understanding of the intuition behind any of them is more important than their names. Continue reading
Machine Learning (ML) should not be a hard read. That is my slogan for this page. With all the respect I have for all the great books out there on ML, I believe the intuition behind most of the ML algorithms can be explained more easily. And that’s what I am trying to do here. I will start with the distance function, I will go through different ML algorithms and try to explain them, do experiments with them or anything which help me to understand them better. Join my journey if you are also interested in ML, and I hope we can help each other learn more.