The main activity of the workshop is the development of mathematical modeling projects by students organized in teams.

The course is organized in three fundamental parts, in addition to some preparation sessions for the presentation of the projects and their evaluation.

Each of the fundamental parts consists of five sessions of two hours each. The first two sessions of each part are dedicated to the presentation of a real life problem and to the introduction of the basic mathematical and computational tools necessary to address the solution of the proposed problem. In the following three sessions of each part of the course, students work in teams to provide a solution to the proposed problem. In these sessions the students are supervised and have the advice of the teaching staff of the subject to complete the projects.

At the end of the course the three projects will be presented in the form of an oral dissertation and a written report.

The projects that will be covered in this course are:

Solving Resource Constrained Scheduling Problems

Hidden structure in noisy data: mixture models, latent variables and probabilistic classification

What Drives Patient Variability when taking a medicine? Accelerating Covariate Discovery in Population Pharmacokinetic Models

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

The mark of the evaluation items 1 will be the same for all members of each team, whereas those of items 2 and 3 have an individual character. In exceptional cases where a component of a team has collaborated clearly less than his/her teammates, his/her grades in item 1 will be multiplied by a factor less than 1.

Items 1 and 2 refer to the organization and expression of the discourse, both in writing (item 1) and in speech (item 2).

The exam (item 3) will deal with the general concepts and illustrative examples addressed in the projects.

The criterion for awarding the grade of "Not Evaluated" is as follows: students will be considered to have been assessed in the course if they have completed both the oral presentation and the written report. Otherwise, the final grade will be "Not Evaluated".

Use of Artificial Intelligence (AI)

For this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively for studying, clarifying concepts, practising, and debugging code. They may also be used to review solutions previously developed by the students themselves. It is not permitted to submit AI-generated content as one’s own work or to use AI during individual assessments unless explicitly authorised. Students must be able to explain and defend any solution, algorithm, or code they submit.

Students must clearly identify which parts have been generated using this technology, specify the tools used, and include a critical reflection on how these tools have influenced both the process and the final outcome of the activity. Failure to disclose the use of AI in this assessed activity will be considered a breach of academic integrity and may result in a partial or total reduction of the grade for the activity, or more severe penalties where appropriate.