Julia Language in Machine Learning: Algorithms, Applications, and Open Issues Kaifeng Gaoa, Gang Meia,∗, Francesco Picciallib,∗, Salvatore Cuomob,∗, Jingzhi Tua, Zenan Huoa aSchool of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China bDepartment of Mathematics and Applications R. Caccioppoli, University of Naples Federico II, Naples, Italy Abstract Machine learning is driving development across many fields in science and engineering. A simple and effi- cient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity. The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. This paper sum- marizes the related research work and developments in the applications of the Julia language in machine learning. It first surveys the popular machine learning algorithms that are developed in the Julia language. Then, it investigates applications of the machine learning algorithms implemented with the Julia language. Finally, it discusses the open issues and the potential future directions that arise in the use of the Julia language in machine learning. Keywords: Julia language, Machine learning, Supervised learning, Unsupervised learning, Deep learning, Artificial neural networks Contents 1 Introduction 4 arXiv:2003.10146v2 [cs.LG] 17 May 2020 2 A Brief Introduction to the Julia Language6 3 Julia in Machine Learning: Algorithms7 3.1 Overview . .7 ∗Corresponding author Email addresses:
[email protected] (Gang Mei),
[email protected] (Francesco Piccialli),
[email protected] (Salvatore Cuomo) Preprint submitted to Computer Science Review May 19, 2020 3.2 Supervised Learning Algorithms Developed in Julia .