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Machine Learning and Neural Networks
Diagrama de temas
General
Syllabus
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