It is expected that students who take the course have a basic knowledge of algebra, probability theory and statistics, as well as computer programming.

After having taken the course, students should have acquired skills of three main types: - Reading, interpretation and understanding of technical and scientific documents in any of the official languages of the course. - Application and adaptation of the techniques described in the course to solving specific problems, being able to evaluate and compare different alternatives and make technical decisions to propose the most appropriate solution at all times. - Reasoning about the decisions to justify and present them to an audience.

This course is organized into the following modules: 1. Computational complexity theory. 2. Semantic Web and linked data. 3. Natural language processing. 3.1. Statistical techniques. 3.2. Techniques based on artificial neural networks. 4. Big data technologies.

The course will be tought with: - Theoretical and practical sessions in which theoretical concepts are explained, with frequent examples and exercises. - Laboratory sessions with computers, in which students will apply the knowledge they acquired to solve problems.