# Visual Simulator for Mastering Fundamental Concepts of Machine Learning
## 2 Related Work and Requirements
### Machine Learning in Teaching at Universities
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Course Title** & **Level of Study** & **University** & **Lab.** & **Tool or Technology for Lab** & **Project** & **Tool or Technology for Project** & **Developed Own Tool** \\ \hline Introduction to & bachelor & Harvard University & + & Python & + & Python & \\ Machine Learning & master & Harvard University & + & Python & + & Python & \\ Machine Learning & bachelor & Stanford University & & & + & Python & \\ Applied Machine Learning & bachelor & Stanford University & & & + & Python & \\ Learning & & Massachusetts & & & & \\ Introduction to & bachelor & Institute of & & + & Python & \\ Machine Learning & & Technology & & & + & Python & \\ Introduction to & bachelor & University of & & & + & Python \\ Machine Learning & & California, Berkeley & & & + & Python \\ Machine Learning & bachelor & Princeton University & & & + & Python \\ Machine Learning & bachelor & Columbia University & & & + & MATLAB, \\ Machine Learning \& & bachelor & California Institute of & & & Python \\ Data Mining & bachelor & Technology & & + & Python & \\ Machine Learning & & bachelor & University of Oxford & & + & Lua, Python \\ and Predictive & master & University of & & & + & R, Python \\ Analytics & & & Chicago & & & \\ Advanced Machine & & & University of & & & \\ Learning and & master & & Chicago & & + & Python \\ Artificial Intelligence & & & & & & \\ Statistical Data & bachelor & Cornell University & + & R & + & R \\ Mining & & & & & + & Python \\ Machine Learning for & bachelor & Cornell University & & & + & R, Python, Java \\ Machine Learning & & & University of & & & \\ Algorithms & master & California, Los Angeles & & & + & Python \\ Introduction to & & & Johns Hopkins & & + & Python, FastDT & + & Python, Java, C\# \\ Machine Learning & master & & University College & & + & Python & + & Seminar paper in \\ and Data Science & & & London & + & Python & + & Latex text editor \\ Introduction to & & & University of & & & + & Python \\ Machine Learning & & & Washington & & + & Python \\ Data Analytics using & & & University of & & + & Python \\ Python & & & California, San Diego & & + & Python \\ Machine Learning & & & University of & & & + & Python \\ Fundamentals & & & & + & & Python \\ Introduction to & & & University of & + & + & Weka & + & Python \\ Machine Learning & master & & University of & & + & Python \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of the structure of machine learning courses at universities in the world. Based on this analysis it can be concluded that the majority of courses offer some form of practical lessons that place emphasis on project tasks and homework. Laboratory exercises are rarely a part of practical evaluation but are more often organized as a demonstration of concrete tools necessary for implementation of project tasks. The technology most frequently used in practical lessons is the programming language Python, followed by the other most popular programming language, R, and the Weka tool [28]. Universities very rarely develop their own tools to support machine learning courses, which means that it is necessary to possess certain programming knowledge to keep up with the course. The analysis shows that highly ranked universities on the ARWU (Academic Ranking of World Universities) list do not use their own tools for teaching, while universities with somewhat lower rankings do develop their own tools for machine learning courses and use them in lessons.