Description

Theory
1

Laboratory
2

Instructors

Luís Manuel Silva

Contents

= Chemometrics =
Q-CP1 - Computational tools for chemometric analysis (Excel, Python including some packages: sklearn, pandas, numpy, matplotlib, seaborn, scipy, etc.)
Q-CP2 - Preprocessing of spectral signals
Q-CP3 - Unsupervised learning methods
Q-CP4 - Supervised learning methods (classification and regression)


= Optimization =
O-CP1 - Computational tools and algorithms for Mathematical Optimization (Excel, MatLab, Octave)
O-CP2 - Optimization of multivariable functions subject to restrictions ; Mono Objective problems
O-CP3 - Optimization of multivariable functions subject to restrictions ; Multi Purpose problems
O-CP4 - Optimization in a Process Simulation environment

Learning Outcomes

= Chemometrics =
Q-OBJ1 - Apply preprocessing techniques in order to improve the quality of the prediction model.
Q-OBJ2 - Apply methods for selecting and extracting characteristics in multivariate data.
Q-OBJ3 - Master algorithms and calculation tools for data analysis in multivariate systems
Q-OBJ4 - Predict categorical and metric properties from classification and regression models.
Q-OBJ5 - Implement existing functions in Python packages to perform tasks in the field of chemometrics.

= Optimization =
O-OBJ1 - Know the calculation methods used in constrained mathematical optimization problems
O-OBJ2 - Master algorithms and computer applications to obtain optimal results in constrained mathematical optimization problems
O-OBJ3 - Be able to solve an optimization problem with constraints, with one or several antagonistic goals
O-OBJ4 - Be able to simulate and optimize a chemical process in a simulation environment and have a critical view of the obtained results