## 5 Final Remarks  
Machine learning applications are rapidly increasing in Industry 4.0 development; thus, it is important for students to learn at least the basics of some ML algorithms. Therefore, Education 4.0 is in charge of introducing future professionals to this area, as well as providing them with the possibility of testing their understanding in real-life scenarios. Hence, the primary objective of this work was to give the students an introduction to Machine Learning and three different classification (supervised) algorithms: K-Nearest-Neighbor, Linear Discriminant Analysis, and Simple Perceptron. This introduction was done through a brief review of the methods, their pseudo-codes, as well as a comparison between them. Through this comparison students can observe that, despite the three methods being able to classify data, LDA and perceptron are only able to deal with binary data, meaning that they are not useful for multi-class classification. Moreover, it is important for students to notice that KNN is able to classify data in any desired number of classes; however, unlike the other two algorithms, this method usually has a high computational cost that increases proportionally to the number of classes and data size. Furthermore, one of the main differences for students to remember between KNN and the other two methods is that the former is able to deal with not-linearly separable data, while the last ones cannot be used for this kind of problem. Furthermore, the only algorithm that has the capacity of giving us the posterior classification probabilities of the input data is the Simple Perceptron, being one of the main advantages of this method when compared to KNN and LDA. Finally, among the three methods, we recall students that the easiest to understand and implement is KNN, followed by Simple Perceptron, and finally LDA. Furthermore, in order to contribute to the development of new and updated educational material that is useful for teachers and students under the Education 4.0 framework, we developed a multidisciplinary test bench by applying these supervised algorithms over the Cleveland Heart Disease dataset, Banknote Authentication dataset, and Wisconsin Breast Cancer dataset to explore their behavior. From the obtained results, students can notice that, for the proposed metrics and datasets, the overall best performance was achieved from the KNN algorithm, followed by LDA and then Simple Perceptron. Furthermore, using MANOVA and Tuckey tests, it can be observed for which parameters the algorithms were statistically different over the presented test bench. Through the previous description of the algorithms and the results of their application over the proposed test-bench, this study provides students with the possibility of learning the basic theory of KNN, LDA, and Simple Perceptron, as well as serving as a guide for the practical application of the acquired knowledge towards the solution real-life problems. Moreover, teachers that are dabbling into the Education 4.0 framework can use this material to introduce their students to Machine Learning theory and applications. Furthermore, it can also be used as an additional reading/exercise that reinforces the understanding of the student posterior to a Machine Learning lesson. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**Metrics**} \\ \cline{2-9}  & **AUC** & **ACC** & **TPR** & **FNR** & **FPR** & **TNR** & **PPV** & **NPV** \\ \hline KNN & 0.9862 & 0.9657 & 0.9807 & 0.0193 & 0.0423 & 0.9577 & 0.9266 & 0.9890 \\ LDA & 0.9908 & 0.9564 & 0.9903 & 0.0096 & 0.0608 & 0.9392 & 0.8919 & 0.9949 \\ Perceptron & 0.9901 & 0.9663 & 1.0000 & 0.0000 & 0.0800 & 0.9200 & 0.8636 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mean value for each metric for each algorithm—Breast Cancer Wisconsin dataset.