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Master en Inteligencia Artificial y Aprendizaje Profundo

PYTHON FOR MACHINE LEARNING

  • Arrays, matrices and vectors
  • Graphics
  • Program flow management
  • Interfaces and data loading
  • Programming exercises

ARTIFICIAL INTELLIGENCE AND DEEP LEARNING

  • History and Evolution of Artificial Intelligence
  • Supervised, unsupervised and reinforced Learning
  • Foundations of Machine Learning
  • Machine Learning Paradigms
  • Extensions

COMPUTER VISION

  • Introduction to CNN
  • CNN architectures
  • Object detection and semantic segmentation
  • CNN for image generation

SEQUENTIAL NETWORKS AND NATURAL LANGUAGE PROCESSING

  • Sequential and time series problems
  • Recurrent networks
  • LSTM and GRU models
  • Attention models and Transformers
  • Applications to Natural Language Processing

GENETIC ALGORITHMS AND EVOLUTIONARY COMPUTATION

  • Introduction to Evolutionary Computation
  • Programming an Evolutionary Algorithm
  • Introduction to Genetic Programming
  • Genetic Programming: Finding the Hidden Function
  • Evolutionary Machine Learning

UNSUPERVISED AND REINFORCED LEARNING

  • Introductions to unsupervised learning
  • Association rules and Recommendation systems
  • Advanced Clustering
  • Introduction to reinforcement learning
  • Markov decision process
  • OpenAI GYM
Master en Inteligencia Artificial y Aprendizaje Profundo

PYTHON FOR MACHINE LEARNING

  • Arrays, matrices and vectors
  • Graphics
  • Program flow management
  • Interfaces and data loading
  • Programming exercises

AUGMENTED INTELLIGENCE AND HUMAN MACHINE INTERACTION

  • Cognitive Theories
  • Interaction design
  • Data and AI Ethics

SEMINARS

  • Seminars on applications of AI in the multiple fields

MASTER’S THESIS

  • Independent research paper performed by the student on one of the topics of the Master. The paper needs to be presented and defended against a Committee at the end of the Master.
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