The students are expected to have basic knowledge of - principles of signal processing - probability and statistics.

Specific technical skills: - Ability to design algorithms that address the classical problems of signal estimation and detection, and statistical model learning. Subject to complexity and performance constraints. - Ability to analyze and assess the complexity and performance of signal processing methods. - Ability to design, integrate and assess complex systems that involve various digital signal processing modules. - Ability to use numerical analysis and data processing software. General skills: - Ability to analyze and summarize problems. - Practical application of theoretical knowledge. - Problem solving. - Team work.

The content of the course has been designed to first give a practical introduction to methods of artificial intelligence and , second , the application of the concepts learned in real problems oriented field of bioengineering and engineering data . This course seeks to strengthen the profile of the future telecommunications engineer with concepts and tools in high demand . Unit 1. Supervised Learning: Unit 1.1. Regression Unit 1.2. Classification Unit 2. Unsupervised Learning Unit 2.1. Clustering Unit 2.2. Dimensionality Reduction

Lectures (1.5 ECTS): Contents: basic theory and methods of signal processing in communications. Methodology: classical lecture with use of slides, videos and white/blackboard. Lab projects (1.5 ECTS): Contents: implementation of algorithms in for the simulation and assessment of digital transmission systems. Methodology: use of numerical software packages (matlab/octave) to study the performance of algorithms and systems.