A Study on Learning Mathematics for Machine Learning

Abstract

This paper is a study on mathematical aspects that can be basic for understanding and applying the contents of machine learning. If you are familiar with mathematics in the field of computer science, you can create algorithms that can diversify researches and implement them faster, so you can implement many real-life ideas. There is no curriculum standard for mathematics in the field of machine learning, and there are many absolutely lacking mathematical contents that are taught in the curriculum presented at existing universities. Machine learning now includes speech recognition systems, search engines, automatic driving systems, process automation, object recognition, and more. Many applications that you want to implement combine a large amount of data with many variables into the components that the programmer generates. In this course, the mathematical areas required for computer engineer (CS) practitioners and computer engineering educators have become diverse and complex. It is important to analyze the mathematical content required by engineers and educators and the mathematics required in the field. This paper attempts to present an effective range design for the essential processes from the basic education content to the deepening education content for the development of many researches.

Fig. 1. Box-plot of Table 4

Fig. 2. Box-plot of Table 5

Table 1. CNN Track

Table 2. RNN Track

Table 3. GAN Track

Tabel 4. Statistics Data of Statistics

Table 5. Statistics Data of Software Architecture

Table 2. Measured value when patient is area Table 6. Statistical Table of μ_a1-μ_a2

Table 7. Statistical Table of μ_b1-μ_b2