Online University Expert in Artificial Intelligence

Create the future with AI: master algorithms, drive change, and lead the technological revolution

Nid: 27957
Syllabus

University Expert in Artificial Intelligence (15 ECTS)

You will explore the most advanced AI techniques and applications to develop innovative solutions:

Course 1: Fundamentals of Artificial Intelligence. 5 ECTS

This course provides a solid foundation in AI, equipping students with the fundamental skills needed to develop AI-based systems.

Course syllabus

1. Introduction to Artificial Intelligence

  • History of AI and basic concepts
  • AI paradigms
  • Ethics and current AI challenges

2. Introduction to Python

  • Fundamentals of Python programming
  • Data manipulation and analysis
  • Libraries for AI development
  • Data visualization using Python
  • Data preparation techniques

3. Practical case study

  • Problem statement and objectives
  • Data collection and preprocessing
  • AI technique selection
  • Implementación de un modelo básico en Python
  • Basic model implementation in Python
  • Model evaluation and improvements

Course 2: Knowledge-Based Systems. 5 ECTS

This course introduces the fundamental concepts of artificial intelligence, starting with search algorithms to understand what type of problems they can solve and what characteristics they have. These algorithms help us design and understand the structure of any Knowledge-Based System.

Additionally, this foundation enables the introduction of Machine Learning concepts, covering supervised learning methods (k-NN, Decision Trees, etc.) and unsupervised learning methods (Clustering).

Finally, the course concludes with semantic web and linked data (graphs). The semantic web allows for the enrichment of data sets through formal knowledge representations: ontologies.

Course syllabus

1. Artificial Iintelligence and knowledge-based systems

  • Introduction to Artificial Intelligence and Knowledge-Based Systems
  • Blind Search Algorithms
  • Heuristic Search Algorithms

2. Machine Learning

  • Introduction to Machine Learning
  • Analogical Learning: KNN
  • Inductive Learning: decision trees, ID3, C4.5

3. Unsupervised learning and semantic web

  • Unsupervised learning: clustering
  • Semantic web and ontologies

Course 3: Artificial Intelligence for Data Science. 5 ECTS

In data science, applying artificial intelligence to a Knowledge-Based System is essential. These methods allow us to explicitly represent knowledge stored in a knowledge base.

This course covers methods and tools of applied artificial intelligence, useful for data analysis and model generation. It completes the Machine Learning concepts and introduces deep learning techniques and advanced artificial intelligence methods.

Course syllabus

1. Machine Learning Algorithms

  • Bagging & Boosting
  • Support Vector Machine
  • Association Rules

2. Deep Learning

  • Multi-Layer Perceptron
  • Convolutional Neural Network
  • Recurrent Neural Network

3. Evolutionary computation

  • Introduction to evolutionary computation
  • Genetic algorithms