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Optimization and Learning


OptlLearn Algorithm

  • An online platform based on learning algorithms that allow to adapt the contents to the needs of the students

Online Resources

  • A set of applets that assist the students in the comprehension of selected mathematical concepts
The iMath Project is addressed to:
  • Math lecturers interested in optimization and learning algorithms and related topics
  • Researchers interested in optimization and learning algorithms and related topics
  • Math students at university level interested in improving their maths skills through the use of a learning environment based on the application of artificial intelligence

iMath Partnership

Latest News

AI in Education


AI in Education

Interested in how to use AI in Education. Why don't you check the tip "AI-driven analytics to assess the effectiveness of educational strategies"?
Use AI-driven analytics to assess the effectiveness of different educational strategies and interventions. Continuously adapt and refine your educational approach based on data-driven insights to improve overall outcomes. 

Fifth Partners' Meeting


Fifth Partners' Meeting

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:

  • Presentation of the new iMath portal with the OptLearn Algorithm
  • Presentation of the created applets
  • Presentation of the uploaded tips
  • Presentation of the template to produce the sustainability report and the testing evaluation report

Applet on hyperparameters on their performance: error’s double descent


Applet on hyperparameters on their performance: error’s double descent

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.

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