Multivariate Data and Meta Modelling: Preparing for Big Data Cybernetics

Preparing for Big Data Cybernetics: The need for understandable, realistic modelling to deal with Quantitative Big Data
Different science traditions: Induction, deduction and the “-ometrics” cultures
Personality types and choice of mathematical modelling style
“Soft multivariate data modelling” by subspace approximation and subspace regression (Dynametrics, chemometrics, qualimetrics): Machine learning, with fast algorithms, graphical interpretation and statistical validation. Bridging between “hard” mechanistic modelling and “soft” data-driven modelling, staying away from “black box” deep learning.
Principal Component Analysis (PCA), ICA, MCR: Analysis of one data table
Ongoing R&D: On-the-fly processing of “everlasting” high-dimensional data streams
Classification, discrimination and cluster analysis
Nonlinear preprocessing, nonlinear soft modelling
Multivariate instrument calibration and PLSR-based selectivity enhancement: “Math is cheaper than physics”
Model validation
Partial Least Squares Regression (PLSR): Analysis of two data tables
Cost-effective statistical design of laborious experiments and massive computer simulations
Multivariate metamodelling to convert mechanistic models (ODEs, PDEs, FEM etc) to bilinear model form, for faster computation, easier overview and safer parameter identification.

Preparing for Big Data Cybernetics: How to discover the Real World
KNOWLEDGE: In-depth knowledge of techniques for multivariate analysis of data. Knowledge of data-driven modelling (metamodelling).
SKILLS: Be able to build models based on experimental data using the aforementioned methods.
GENERAL COMPETENCE: Skills in applying this knowledge and proficiency in new areas and complete advanced tasks and projects. Skills in communicating extensive independent work, and master the technical terms of multivariate analysis. Ability to contribute to innovative thinking and innovation processes.

Lectures, mandatory group work and project.

Compulsory assignments
• Exercise