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The fifth partners' meeting of the iMath project took place in Malaga (ES). All partners participated in the meeting and the main discussed topics were:
The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms.In particular, you could study the error double descent phenomenon when approximating a function with a polynomial regressor: test error first decreases, then increases, and then decreases again, increasing model's polynomial degree. The phenomenon occurs under specific conditions, but the user will be able to modify regularization's parameter and polynomial degree and change the training dataset to play with the regressor and see what happens on the error’s curve.The applet has been realized using Dash and Plotly.
The partnership produced a paper on the iMath project. This paper presents the aims and activities of the iMath project and, as one specific activity, the OptLearn algorithm. The algorithm provides student activity recommendations with the aim of maximising their learning outcomes at a given student’s time effort. It is based on automatic concept map generation BFS graph walk. The paper is available at https://erk.fe.uni-lj.si/2023/papers/kosir(student_activity).pdf
The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms.In particular, you could test Support Vector Machine (SVM) algorithm on a classification task. You can choose between three different datasets (Moons, Circles, linearly separable) and set noise, number of samples and distance between classes, and you can tune the main model’s hyperparameters: kernel type, kernel parameter γ, regularization parameter C and the approach to adopt in muti-class case (one-vs-one, one-vs-rest).The applet has been realized using Dash and Plotly.
The fourth partners' meeting of the iMath project took place in Hamburg (DE). All partners participated in the meeting and the main discussed topics were:
K-means is a clustering method widely recognized in the literature and used to subdivide a set of data into distinct groups called clusters. The purpose of this algorithm is to assign each element of the data set to clusters so that elements within the same cluster are more similar to each other than to elements in other clusters.
The K-means process can be summarized as follows:
This applet aims to provide a visual tool for examining different critical valuesand provide insights into the evaluation of a concept map’s quality and itsvisual representation. We focus on salience metrics to assess and help usersenhance the effectiveness of their concept maps with an evaluation of itsquality. The applet features an Interactive Graph Visualization by leveraging thePython library “dash-interactive-graphviz”, allowing the user to seamlesslyzoom in or out, to check in an easy manner the different graph layouts thatthe user can choose. It also allows the user to click on nodes to get specificmetrics and information of the node. Although two sample concept maps areprovided and the user can explore them, it also allows the user to upload aCSV file to the applet for working with its custom concept maps.Furthermore, the applet has multiple buttons with question marks that whenclicked, displays information about the usage and the shown metrics. By usingBootstrap, the applet has a responsive design and allows using it insmartphones.
The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms.In particular, you could test Random Forest (RF) on a classification task. They can choose between three different datasets (Moons, Circles, linearly separable) and set noise, number of samples and distance between classes, but most of all they can tune the main model’s hyperparameters: number of trees in the forest and number of features to consider deciding the best split. The applet has been realized using Dash and Plotly.
The third partners' meeting of the iMath project took place in Florence (IT) . All partners participated in the meeting and the main discussed topics were:
The second partners' meeting of the iMath project took place in Patras (EL) . All partners participated in the meeting and the main discussed topics were:
The database of benchmarks related to learning and optimization algorithms. Join the iMath Community and submit your proposal!
The database of algorithms and terminology related to machine learning and optimization algorithms is available on the iMath Portal. Join them on the iMath Community and submit your proposal!
Math Lecturers and Researchers can now contribute to the iMath Library dedicated to scientific and academic publications onlearning and optimization algorithms. Join them on the iMath Community and submit your proposal!
The brochure of the iMath project is available on the project website in English and in all partners languages. The brochure, based on the visual identity of the project, describes the context, the aim, the activities, the target groups, and the expected results of the project.
Each national version also indicates the contact details of the national partner.
It is now possible to register to the iMath platform and contribute to the database of publications, algorithms, benchmarks and students' learning indicators. What are you waiting for? Share your results with your colleagues all around the world!
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The kick-off meeting of the iMath project took place online as a consequence of the COVID-19. All partners participated in the meeting and the main topics discussed were:
The iMath project is funded, by the European Commission through the Portuguese National Agency for the Erasmus+ Programme to develop a new AI-driven tool that supports higher education math students, providing them with a database of resources, hands-on activities, self-evaluation tests based on their previous performances.