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Interdisciplinary Learning Courses

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence Introduction to Artificial Intelligence
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Interdisciplinary Learning CoursesIntroduction to Artificial Intelligence

Artificial intelligence will influence all your professional careers

The concept of this course is to bring artificial intelligence to students of various disciplines. Artificial intelligence projects are multidisciplinary, therefore, they not only need profiles of the mathematical/analyst type or programmers, but they also require an expert in the field where they are applied.
The course will show practical cases of the use that is being made of Artificial Intelligence in many fields (engineering, medicine, architecture, art, computing, finance, industry, transportation, music, leisure, communication, business...), and will explain what is behind each of these applications.

It is important that students are aware of how artificial intelligence will sooner or later influence their professional career.
At the end of the course, each student will be able to propose an artificial intelligence project applied to their field.
The course does not require advanced knowledge of mathematics, statistics or programming. We will treat the algorithms from a totally conceptual and applied point of view, with the idea of understanding when they are used and what parameters control them.


Learning objectives


  • Know the roles within an artificial intelligence project.
  • Understand the architecture of an artificial intelligence project.
  • Know the different methodologies used in artificial intelligence projects.
  • The student must be able to propose an artificial intelligence project in their field.
  • Demystify the use of artificial intelligence and be able to adapt it to our surroundings.

Contents of the Subject

Introduction to Artificial Intelligence

  • 1- History and introduction to artificial intelligence
  • 2- Machine learning
  • 3- Types of learning: supervised, unsupervised, reinforced.
  • 4- Terminology: instances and features
  • 5- Datasets
  • 6- Learning or training
  • 7- Evaluation.
  • 8- Classification algorithms. Naive Bayes, SVM, Decision trees.
  • 9- Regression
  • 10- Clustering.
  • 11- Deep Learning.
  • 12- Neural networks and applications
  • 13- Tools, programming languages and specific AI libraries.
  • 14- Practical cases

Professor

profesor Professor of Introduction to Artificial Intelligence

Javier Sánchez Sierra

Director de Máster de Ingeniería Industrial. Doctor Ingeniero Industrial por la Universidad de Navarra. Trabaja en Widia-Kennametal de 1996 a 1999 en mecanizados especiales para automoción y aeronáutica. De 2000 a 2008 es profesor investigador en la Universidad de Navarra, en temas de computación, programación CNC, rapid prototyping, diseño mecánico, modelado 3D y visualización. Su tesis doctoral se centra en el diseño y representación gráfica de estructuras tensadas, tras lo cual realiza un master en Estructuras Tensadas (Archineer) en Dessau, Alemania. Del 2008 al 2011 realiza estancia post-doctoral en la Universidad de Stanford, California (Center for Computer Research in Music and Acoustics) donde colabora en proyectos multidisciplinares relacionados con música, composición, procesado de audio, performances, visualización. Desde 2012 se dedica a la programación de dispositivos móviles (como desarrollador freelance, con más de 30 apps en el AppStore) y es co-fundador de varias startups. Es profesor asociado en el Berklee College of Music (Campus de Valencia) y director técnico (CTO) de Flits Music GmbH y Tegus Medical GmbH, con sede en Hamburgo, Alemania.