## 1 Introduction  
Currently, society is going through the fourth industrial revolution, also known as Industry 4.0. This new revolution is being driven, principally, by advancements in areas such as artificial intelligence (AI), robotics, cloud computing, the internet of things (IoT), cyber-physical systems, big data, etc. [1, 2]. Thus, industries have used these technologies to provide solutions to the growing needs of humankind; however, in order for them to continue adapting their ecosystems to the digital world, they need qualified professionals with knowledge and skills in those areas. Consequently, in order to generate skillful personnel, Education 4.0 is being proposed as a new framework so that schools are able to train professionals who are capable of creating knowledge through scientific research and experience, as well as sharing this knowledge with society and using it to face technological, social, political, and economic challenges [3]. Hence, it has been of great importance for universities to develop new curricula at the undergraduate level that guide future professionals towards the requirements of Industry 4.0 [4]. Some authors have explored and proposed guidelines for the creation of newly updated curricula that are in line with the upcoming revolution. According to [3], it is important for Education 4.0 paradigm to be based on competency development so that students do not only memorize and repeat data but also learn how to use it through learning experiences. In other words, students must learn by doing, using, and interacting with material that encourages them to apply the acquired theoretical knowledge [5]. In this paradigm, we can consider the term \"competency\" as the set of skills, values, and knowledge that allows the student to successfully perform a given task or activity [6]. In order to develop the Education 4.0 curricula for engineers, ref. [3] identified ten different competencies that are necessary for students to develop, which are: virtual collaboration, resilience, social intelligence, novel and adaptive thinking, load cognition management, sense-making, new media literacy, design mindset, transdisciplinary approach, and computational skills. However, in order to fully exploit these competencies, students must also acquire knowledge on how to apply them towards the usage of the latest technologies that conform the basis of Industry 4.0, such as Machine Learning (ML). Machine Learning is a subset of artificial intelligence that builds a mathematical model based on \"training data\" to predict an outcome or perform a given task without the machine being explicitly programmed to do it [7]. ML learning applications over Industry 4.0 development cover diverse areas such as healthcare, economics, energy engineering, among others. In healthcare, Machine Learning predictions have been used to inform diagnoses, decide clinical care pathways, and stratify the risk of a patient having a given disease [8]. Examples of these applications include diagnosis of heart diseases on diabetic patients [9], prediction of diabetes disease [10; 11], breast cancer prediction and diagnosis [12; 13], thyroid disease diagnosis [14], and lung and brain tumor detection [15; 16]. In economics and finance, ML algorithms have been applied for portfolio design [17; 18; 19], forecasting financial time series [20], and stock market analysis [21; 22; 23]. In energy engineering and management, Machine Learning models have been used to characterize petroleum reservoirs [24], solar radiation, wind power and energy consumption forecasting [25; 26; 27], and reactor control optimization [28]. Some of these tasks are often accomplished through an ML branch known as classification. In ML, classification is a supervised learning technique in which the computer is fed with labeled data and learns from it so that, in the future, it can use this learning to classify new data. The classification may be binary (only two classes) or multi-class (more than two classes).