Statistical models in engineering

This page is dedicated to the short course Statistical Modelling for Optimization, which is held from the 27th to the 30th of June 2017 at the Universidad Tecnológica de Pereira (Colombia).

The concepts discussed during the lectures are illustrated on a catapult optimization case study. The aim is to find the set of parameters (4 variables) maximizing the distance of the throw.

Lectures:

• Lecture 1 - Statistical models in engineering, slides in pdf, shiny demo here
• Lecture 2 - Design of Experiments, slides in pdf
• Lecture 3 - Gaussian process regression, slides in pdf
• Lecture 4 - Global optimization, slides in pdf

Lab sessions:

• Lab 1 - Linear regression and Gaussian process regression, Lab sheet (pdf), python files (zip)
• Lab 2 - Design of Experiments, Lab sheet (pdf), python files (python)
• Lab 3 - Gaussian process regression, Lab sheet (pdf), python file (python)
• Lab 4 - Efficient Global Optimization, Lab sheet (pdf), python file (python)

Source code:

• All the latex and python source code related to this project are available on this github repository.