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The theory of Hard Margin Support Vector Machines (SVMs) is explained in an easy-to-understand manner.SVMs are a type of supervised machine learning algorithm for pattern identification. It is an excellent two-class classification algorithm with the idea of "maximizing margins."

Derive the distance between a point and a hyperplane, which is the general form of the distance between a point and a line.

When studying machine learning theory, we often see the operation of "differentiating a scalar by a vector. In this article, we derive the formula for "differentiating a scalar by a vector.

The theory of logistic regression is explained in simple terms. Estimating the value of a parameter based on n pairs of observed data yields a model that outputs the probability of a response to any given numerical level.

The theory of principal component analysis (PCA), a method of dimensional compression, is explained. Principal Component Analysis is a method to summarize the information of multidimensional data observed on mutually correlated features into new features expressed as a linear combination of the original features without losing any information as much as possible.

Consider the transformation of a random variable X into Y = g(X) to derive the form of the transformed probability density function.

The binomial theorem is a very versatile theorem that shows up in a variety of places. In this article, we will discuss the binomial theorem and its generalization, the polynomial theorem.