On the 8 and 9th of February 2018, we gathered with an international group of process mining researchers to discuss the many challenges in the field of conformance checking. The event was organized by the Flemish Scientific Research Community on Process Mining (htpps://www.srcprocessmining.org), a community that is led by our research group, together with KULeuven and UGent. The brainstorm session was attended by researchers from the Polytechnic University of Catalonia, the Pontifical Catholic University of Chile, RWTH Aachen University, the Technical University of Eindhoven and the Wirtschaftsuniversität Wien.
There is no doubt about the current role of Machine Learning in the fascinating world of Business Intelligence. Predicting whether a customer will be loyal to the company or not, understanding customers’ behavior or anticipating market fluctuations are typical examples on which Machine Learning may be pivotal. Unfortunately, most successful Machine Learning algorithms like Random Forests, Neural Networks or Support Vector Machines do not provide any mechanism to explain how they arrived at a particular conclusion and behave like a “black box”. This means that they are neither transparent nor interpretable. We could understand transparency as the algorithm’s ability to explain its reasoning mechanism, while interpretability refers to the algorithm’s ability to explain the semantics behind the problem domain.
Teaching R to students with little to no experience in programming or data analysis is a challenging task. Our talk at useR!2017 showed how different ingredients of our course Exploratory and Descriptive Data Analysis at UHasselt are used to facilitate the learning of R.
Firstly, the educational environment at UHasselt, based on guided self-study and the use of small group working sessions, allows each student to have an individual pace of learning and gives them frequent feedback.
The problem we are addressing originates at a transportation company. When analyzing the results of their shipments, they would like to break this down into categories. For example, how many packages contain clothing and how many contain laptops, and does different content lead to different results during shipment. Instead of having to manually label all these packages, we look for a more efficient solution. The goal of this blog post is to show you the steps we took to solve this problem using machine learning. Steps that can be followed when you undertake your own machine learning projects.
The world of education is changing more than ever. Since the emergence of the internet, platforms for e-learning are becoming omnipresent. The use of video lectures and other online learning materials are rapidly replacing traditional lectures and textbooks. As learning is occurring more and more online, teachers are facing new challenges. The increasing distance between student and instructor asks for new tools to track and adjust learning activities.