Syllabus

      1. Intelligence and learning
        • What is intelligence?
        • What are intelligent machines?
        • The learning relevance
        • Building intelligent machines
        • Objectives of the subject
        • Applications
      2. Feature processing
        • Objectives of feature processing
        • Quality criteria
        • Feature selection
        • Unsupervised linear processing
        • Supervised linear processing
      3. Classical classifiers
        • Objective of classifiers
        • Classifier types
        • Supervised classifiers
        • Unsupervised classifiers
      4. Machine learning general methodology
        • Objectives
        • Supervised and not supervised learning
        • Learning challenges
        • Building machine learning models
        • Errors and validation
      5. Bio-inspiration
        • Intelligence and the cortex
        • Cortex structure
        • Visual intelligence
        • Visual cortex
        • Cortex working conclusions
      6. Supervised Neural Networks: Multilayer Perceptron
        • Artificial Neural Networks
        • Perceptron and the MLP structure
        • The back-propagation learning algorithm
        • MLP features and drawbacks
        • The auto-encoder
      7. Non supervised Neural Networks: Self-organizing Maps
        • Objectives
        • Learning algorithm
        • Examples
        • Applications
      8. State of the art, research and challenges