OBJECTIVES 1.- General/Cross-curricular objectives 1.1. General basic knowledge 1.2. Analysis and synthesis abilities 1.3. Capability of applying the knowledge they have acquired 1.4. Problem-solving skills 1.5. Capability of integration of knowledge 2.- Specific learning objectives (Knowledge-related objectives) 2.1. Mathematical basis of signal processing 2.2. General knowledge on potential text, speech, image, and video processing applications in the big data framework. 2.3. Basic subsystems of signal processing applications in the big data framework. (Instrumental objectives) 2.5. Use of signal processing software. 2.6. Mastering of basic processing tools in the big data framework. 2.7. Solving signal processing problems in the big data framework by using several basic tools. (Attitudinal objectives) 2.8. Individual- and team-work 2.9. Decision-making 2.10. Analysis and problem-solving capabilities

The aim of this subject is to describe automatic tools for analysis of text, speech, audio, image and video in the Big Data framework, according to a Project-Based Learning approach. Additionally, some sessions of the subject will be devoted to discuss an issue of great social impact, such as health, from the perspective of data analytics. The subject will have a practical orientation, combining theoretical and lab sessions, so that the students will be able to do some practical work on which they will be evaluated. Technologies will be described first; then, their applications, combining theory with practical work on the lab when possible. Emphasis will be placed on applications in the Big Data framework, with the aim of motivating the use of automatic analysis tools from a realistic perspective. The syllabus is as follows. It focuses on multimedia information analytics (vs. analysis). In this field there is an extensive use of mathematics and statistics to extract valuable knowledge from data¿data analysis, going beyond and using the insights from data to generate recommendations, to guide a decision making process, etc. 0.- Course Presentation 1.- Image and Video Analytics 1.1.- Basic concepts in image and video processing 1.2.- Image categorization 1.3.- An introduction to CNNs for image and video analytics 2.- Speech Analytics 2.1.- Fundamentals of Speech Analysis 2.2.- Speech Technologies 3.- Big Data in Health 3.1.- From Ambulatory Monitoring to the Healthy Apps

Two teaching activities are proposed: theoretical classes with examples and lab exercises. THEORETICAL CLASSES WITH EXAMPLES (1.5 ECTS) The theoretical class will be given in the blackboard, with slides or any other means to illustrate the main concepts of the course. The explanations will be completed with examples, demonstrations and students' presentations of topics and practical cases related to the content of the course. LABORATORY EXERCISES (1.5 ECTS) Some basic concepts will be studied in the lab. The students will participate actively in the implementation of the solution. There will be two types of lab exercises: - Guided lab exercises - Project: solving problems by means of automatic analysis of multimedia information.