- Intelligence and learning
- What is intelligence?
- What are intelligent machines?
- The learning relevance
- Building intelligent machines
- Objectives of the subject
- Applications
- Feature processing
- Objectives of feature processing
- Quality criteria
- Feature selection
- Unsupervised linear processing
- Supervised linear processing
- Classical classifiers
- Objective of classifiers
- Classifier types
- Supervised classifiers
- Unsupervised classifiers
- Machine learning general methodology
- Objectives
- Supervised and not supervised learning
- Learning challenges
- Building machine learning models
- Errors and validation
- Bio-inspiration
- Intelligence and the cortex
- Cortex structure
- Visual intelligence
- Visual cortex
- Cortex working conclusions
- 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
- Non supervised Neural Networks: Self-organizing Maps
- Objectives
- Learning algorithm
- Examples
- Applications
- State of the art, research and challenges