Introduction 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
Professor of Introduction to Artificial Intelligence