TK8116 - Multivariat data- og meta-modellering

Om emnet

Vurderingsordning

Vurderingsordning: Muntlig eksamen
Karakter: Bestått/Ikke bestått

Vurderingsform Vekting Varighet Hjelpemidler Delkarakter
Muntlig eksamen 100/100 D

Faglig innhold

Forleses høst 2017

Læringsutbytte

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.

Obligatoriske aktiviteter

  • Øvingsoppgave

Spesielle vilkår

Vurderingsmelding krever godkjent undervisningsmelding samme semester. Obligatorisk aktivitet fra tidligere semester kan godkjennes av instituttet.

Forkunnskapskrav

Desire for mental overview, statistical validity and search for unexpected patterns and causalities.

Good understanding of basic vector and matrix algebra.

Kursmateriell

Martens H (2015) Quantitative Big Data: Where Chemometrics can contribute, J. Chemometrics 15; J. Chemometrics 2015; 29: 563–581 1 http://onlinelibrary.wiley.com/doi/10.1002/cem.2740/epdf
Kristin Tøndel and Harald Martens (2014) Analyzing complex mathematical model behavior by PLSR-based multivariate metamodeling. WIREs Computational Statistics, Volume 6, Issue 6, pages 440–475, November/December 2014. DOI: 10.1002/wics.1325
Harald Martens (2011): The informative converse paradox: Windows into the unknown. Chemometrics and Intelligent Laboratory Systems 107 (2011) 124–138
Raffaele Vitale, Anna Zhyrov, João F. Fortuna, Onno E. de Noord, Alberto Ferrer, Harald Martens (2017): On-The-Fly Processing of continuous high-dimensional data streams. Chemometrics and Intelligent Laboratory Systems 161 (2017) 118–129

Eksamensinfo

Vurderingsordning: Muntlig eksamen

Termin Statuskode Vurderingsform Vekting Hjelpemidler Dato Tid Rom *
Høst ORD Muntlig eksamen 100/100 D
Vår ORD Muntlig eksamen 100/100 D
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