The objective of the course is to provide the student with the necessary knowledge about advanced data analysis techniques, including machine learning, data mining, Artificial Intelligence, and their application in IoT-related sectors. In the same way, the student should know the principles and methods related to these techniques and their applicability in various environments.

1. Introduction 2. Data Mining & Machine Learning 3. Methodologies 4. Exploration of the data 5. Regression & Classification 6. Clustering 7. Other topics (Incremental learning, Time series, Text Analytics)

FORMATION ACTIVITIES: - Theoretical classes. - Laboratory practices. - Teamwork - Individual student work METHODOLOGY: - Exhibitions in the teacher's class with the support of computer and audiovisual media, in which the main concepts of the subject are developed, and the bibliography is provided to complement the students' learning. - Critical reading of texts recommended by the teacher of the subject: Press articles, reports, manuals, and/or academic articles, either for further discussion in class or to expand and consolidate the knowledge of the subject. - Resolution of practical cases, problems, etc., raised by the teacher individually or in a group. - Preparation of papers and reports individually or in groups. - Exposition of projects in virtual classrooms. - Participation in discussion forums. TUTORIALS: - Individual tutorials that will allow the student to consult individually with the professor specific doubts about the subject of the program and the exercises/problems proposed. - Group tutorials that will allow work teams to resolve doubts related to group assignments.