DATA DESIGN AND NATURE-INSPIRED COMPUTING

Academic year
2016/2017 Syllabus of previous years
Official course title
DATA DESIGN AND NATURE-INSPIRED COMPUTING
Course code
CM0478 (AF:230405 AR:111659)
Modality
On campus classes
ECTS credits
6
Degree level
Master's Degree Programme (DM270)
Educational sector code
SECS-S/01
Period
1st Semester
Course year
1
Where
VENEZIA
High dimensional data, namely data characterized by a large number of variables, are analyzed in this course assuming the hypothesis that just few sets of data are relevant for the problem under study, avoiding in this way collection and analysis of large dat sets. To uncover these sets of data, that we call “ intelligent data” we will adopt both classical approaches built with statistical regression models, and evolutionary approaches where the search is driven by the information achieved by models.
The statistical design of data.

High dimensionality and variable selection.

Multivariate regression model and Lasso estimator.

Nature-inspired computing and the evolutionary approach.

Genetic algorithms.

Evolutionary Bayesian networks.

Model-based evolutionary algorithms.
Bühlmann, P., Van De Geer S., Statistics for high dimensional data, 2011, Springer.

Elben A. E., Smith J. E., Introduction to Evolutionary Computing, 2015 (second Edition), Springer.

Baragona, R., Battaglia, F., Poli, I., Evolutionary statistical procedures, 2010, Springer.
written and oral
There will be required readings associated to each lecture. Most reading will be from the course text but students are encouraged to seek supplementary material.
Home-works will be assigned and graded. In addition challenging problems will be assigned and students will be encouraged to work together (group-work). The results of these problems will be evaluated at the exam session.
All the slides presented in the lectures will be available in the course website.
English
  • Course with sustainable contents
  • University credits of sustainability: 6
  • Lecture notes, material for reference or for self-assessment available online or as e-book
  • Use of virtual forum, blog or wiki
  • Use of open-source software