In this course, students will examine various topics on research methodology, specially focused on artificial intelligence and deep learning. In particular, starting with the presentation of topics related to the publication of results, writing publications, presentation and dissemination of their work, they will learn about the importance of creating and using datasets, the quality of information as well as the results and effects on the modelling accuracy, and how to choose the right tools for data exploitation. Moreover, they will learn about the regulatory and legal framework for personal data protection, and how their research can comply with privacy rules, as well as ethical issues regarding the collection and use of data related to personal activities.

• Writing a scientific publication, methods of writing, literature research, logical structure, avoiding plagiarism, repositories, choosing the appropriate publication channel (conference, journal, etc.), relevance and heterogeneity.
• What is considered as personal data, GDPR, data owner consent, data depersonalization
• Data quality and characterization, methods of creating and augmenting datasets

• Writing and cross-validating a mini report based on specific material on an AI related topic
• Framework description for the collection, processing and use of personal data for the development of AI models