We present here the first practical project, part of the students’ evaluation process of the Advanced Machine Learning course of the Master in Engineering and Data Science of the University of Coimbra. This work is to be done autonomously by a group of \textbf{two students}. The deadline for delivering the work is **7 Abril of 2024** via Inforestudante.
The quality of your work will be judged as a function of the value of the technical work, the written description, and the public defence. All sources used to perform the work (including the code) must be clearly identified. The document may be written in Portuguese or in English, using a word processor of your choice (Latex is preferred). **The written report is limited to 8 pages long**.
The document should be well structured, including a general introduction,  a description of the problem, the approach, the experimental setup, an analysis of the results, and a conclusion. The report should follow the Springer LNCS format. The Latex and Word templates are available in the Support Material of the course. The final mark will be given to each member of the group individually.
To do the work the student may consult any source he/she wants. Nevertheless, plagiarism will not be allowed and, if detected, it will imply failing the course. While doing the work and when submitting it, you should pay particular attention to the following aspects (whose relative importance depends on the type of work done):

- description of the approach to the problem
- description of the general architecture of the methods used;
- description of the experiment, including a table with the parameters used which should allow full replication;
- description of the evaluation metrics used for the validation: quality of the final result, efficacy, efficiency, diversity, or any other most appropriate;
   

Do not forget, besides what was just said, that it is fundamental: (1) to do a correct experimental analysis; (2) to do an informed discussion about the results obtained; (3) to put in evidence the advantages of the chosen alternative.