If you have ever participated in a machine learning competition (like those held by Kaggle), you may be familiar with the geometric mean as a simple method that combines the output of multiple models. In this post, I will review the geometric mean and its relation to the Kullback–Leibler (KL) divergence. Based on this relation, …
From Geometric Mean to KL Divergence to Noisy Labels Read More »
In the previous post, I wrote about expectation, a great tool that helps us capture notion of “center” for a random variable. In this post, I will write about variance, another great tool that represents the “spread” of a random variable. Consider a random variable with two probability density functions (PDFs) and , shown in …
Machine learning (ML) is filled with various forms of mathematical expectations. It is not exaggerated to say that almost all training objective functions in ML are in the form of an expectation. In this blog post, I will review some basics on expectations to create a foundation for my next posts on probabilistic ML. I …
Machine learning (ML) with all its hypes and promises is an extremely interesting area of research. Its study in the most fundamental form requires a strong background in mathematics, statistics, computer science, and engineering. But, when it is applied to real-world problems, it even requires further knowledge in different application domains such as medicine, biology, chemistry, …