Information
Course Code ECS05
Semester 2
Category
ECTS Credits 8
Eclass
Professors

Koukiou G.

Proposed Bibliography
Course Description
  1. Basic principles of classic classification theory (Bayes). Probability ratio as criterion in population distinction. Use of the theory in standard (Gaussian) deviate populations.
  2. Mahalanobis Distance: Separation of the feature space according to the population statistics and the correlation of the features.
  3. Correlation of population features: Degree of correlation. Feature quality. Dimensionality of a classification problem. Dimensionality reduction and important dimensions.
  4. Artificial Neural Networks: Problems that they can solve. Neural networks simple structures.
  5. Parameters Estimation. Calculation of the distribution of dependent random variables:
  6. Mathematical Morphology
  7. Signal detection theory: Basic concepts. Neyman-Pearson criterion. Constant false alarm rate (CFAR) detector.
  8. Information synthesis, into simple data, into features and into decisions.
  9. Remote sensing
  10. Examples of remoting-sensing information synthesis.
  11. Decision synthesis.
  12. Oversampling – Noise shaping – ΣΔ coders