About the Multivariate Data Analysis and Machine Learning group

• Research areas

  We have a background from established local scientific environments within data science, statistics and chemometrics, with important contributions in development and applications of multivariate analysis methods. Data analysis related to food products, breeding data, non-destructive rapid measurements, classification of microorganisms and medical diagnostics are among our applications. The data can be based on spectroscopy, genotyping, chromatography, rheology, 2D- and 3D images, lidar, etc.

  Our research focus is on development, efficiency and application of multivariate methods for prediction and analysis of data with many explanatory variables.

  The group has an expressed ambition of contributing to development of powerful analysis methods with applications in relevant research environment all over Campus Ås.

• Projects

  • Machine Learning (Postdoc): Research and course development on highly multivariate and time dependent data
  • Data driven approach for prediction of power flexibility
• Publications

  Ulf G. Indahl	Cristin, ResearchGate, Google Scholar
  Kristian H. Liland	Cristin, ResearchGate, Google Scholar
  Oliver Tomic	Cristin, ResearchGate, Google Scholar
  Tormod Næs	Cristin, ResearchGate, Google Scholar

• Course development

  Courses developed by members of the group since 2015:

  • IND320 – Data to decision
  • INF100 – Principles of Information Processing
  • MATH-INF110 (now DAT110) – Introduction to Data Analysis and Visualisation
  • MATH310
  • MATH280 – Applied Linear Algebra
  • DAT200 – Applied Machine Learning
  • DAT300 – Applied Deep Learning
  • DAT320 – Sequential and Time Series Data Analysis
  • DAT350 – Applied healthcare data science and medical physics
  • MLA210 – Machine Learning With Examples From Technology and Finance
  • MLA310 – Matrix Methods for Data Analysis and Machine Learning
• Group members

  External group members

  Tormod Næs

  Nofima

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