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Fundamentos de la Inteligencia Artificial

General information

Closing: December 20, 2024

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This course studies the basic techniques of artificial intelligence from the point of view of data science, that is, as a mechanism for extracting knowledge and providing value from such data. The main objectives of the course are the following:

  • To become familiar with the different paradigms of machine learning.
  • To learn the principles of visual analytics as a complement to the data analysis process.
  • To learn how to use various tools to create machine learning models in a practical way.
  • Understand the fundamentals of supervised models, including classification and regression models.
  • Understand the fundamentals of some of the most widely used unsupervised learning algorithms.
  • Understand some modern neural network models and their applications. Suitable graduation profiles for the content of the course are data scientist, data engineer or artificial intelligence engineer, among others.
Methodology: 

Manual structured in different sections for progressive learning, complementary explanatory videos.

Bibliography: 
  • Russell, S. J., y Norvig, P. (2004). Inteligencia Artificial: un enfoque moderno. Pearson.
  • Hastie, T. et al. (2001). The elements of statistical learning. 2001.
  • Bishop, C. (2006). Pattern recognition and machine learning. Springer.
  • Tufte, E. (1973). The visual display of quantitative information. Graphics Press USA
  • Tufte, E. R., Goeler, N. H., & Benson, R. (1990). Envisioning information (Vol. 2). Cheshire, CT: Graphics press.

Programming

  • Conferencia magistral Duración: 1min
    "Introducción"
    Guillermo Hernández, BISITE
  • Conferencia magistral Duración: 25min
    "Introducción a la IA y la analítica visual"
    Guillermo Hernández, BISITE
  • Conferencia magistral Duración: 9min
    "Software Orange"
    Guillermo Hernández, BISITE
  • Conferencia magistral Duración: 12min
    "Deep Intelligence"
    Guillermo Hernández, BISITE
  • Conferencia magistral Duración: 12min
    "Jupyter"
    Guillermo Hernández, BISITE
  • Conferencia magistral Duración: 1min
    "Conclusión"
    Guillermo Hernández, BISITE
  • Conferencia magistral Duración: 3min
    "Aprendizaje supervisado"
    Cristina Santa Cruz González, BISITE
  • Conferencia magistral Duración: 11min
    "Clasificación"
    Cristina Santa Cruz González, BISITE
  • Conferencia magistral Duración: 32min
    "Regresión"
    Cristina Santa Cruz González, BISITE
  • Conferencia magistral Duración: 13min
    "Ejemplo Regresión"
    Cristina Santa Cruz González, BISITE
  • Conferencia magistral Duración: 3min
    "Introducción al aprendizaje no supervisado"
    María Alonso García, AIR
  • Conferencia magistral Duración: 6min
    "Clustering"
    María Alonso García, AIR
  • Conferencia magistral Duración: 10min
    "Reglas de asociación"
    María Alonso García, AIR
  • Conferencia magistral Duración: 14min
    "Reducción dimensional PCA"
    Cristina Santa Cruz González, BISITE
  • Conferencia magistral Duración: 3min
    "Introducción al Deep Learning"
    Juan Herranz Martín, AIR
  • Conferencia magistral Duración: 6min
    "Perceptron"
    Juan Herranz Martín, AIR
  • Conferencia magistral Duración: 5min
    "Deepint"
    Juan Herranz Martín, AIR
  • Conferencia magistral Duración: 5min
    "CNNRNN"
    Juan Herranz Martín, AIR
  • Conferencia magistral Duración: 1min
    "Conclusión"
    Juan Herranz Martín, AIR

Teachers

María Alonso García

Guillermo Hernández

BISITE

Juan Herranz Martín

Cristina Santa Cruz González

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Price: 

Gratuito

graduation
Modality: 
Online
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Language:
Spanish
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Hours: 
100
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Duration: 
3 months