Learning goals.
The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents.
The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning).

Knowledge and understanding.
Acquire the basics of machine learning techniques.
Understanding how and why to choose between alternative methods, or possibly how to combine different methods.
Ability to handle large amounts of images or text with the help of appropriate open source software.

Applying knowledge and understanding.
Students develop critical skills through the application of a wide range of statistical and machine learning models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Making judgements.
Students develop critical skills through the application of a wide range of machine learning and statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the analysis of the images or text documents by machine learning techniques.