TK8117 - Multivariate Data Analysis - Advanced Topics


Examination arrangement

Examination arrangement: Oral examination and work
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Work 30/100
Oral examination 70/100 D

Course content

  • Design of Experiments
  • Principal Component Analysis
  • Multivariate regression methods (MLR,PCR,PLSR)
  • Strategies for model selection and validation (bias-variance trade-off)
  • Features and variables selection
  • Classification methods (Machine learning)
  • Time series analysis
  • Prediction Error Methods for the Identification of dynamical systems
  • Kalman filters
  • Metamodelling & hybrid modelling
  • Compressed sensing
  • Independent Component Analysis
  • PARAFAC, multiblock (sensor fusion) and IDLE modelling

Learning outcome

KNOWLEDGE: The students shall get an overview of different methods for analysing data from processes that are continuous and/or time dependent, both for quantitative prediction and classification. They shall be able to plan practical experiments using statistical principles. This includes sensor-fusion and hierarchical modelling of multiblock data. SKILLS: The students shall be able to organize data form different types of measuring instruments, with different dimensions and consider optimal pretreatment of data. They shall be able to propose the most suitable methods given a specific application. GENERAL COMPETENCE: Be able to use knowledge and skills on new applications. Be able to discuss topics related to the course with specialists in the topics and propose which methods from the course to use in interdisciplinary projects.

Learning methods and activities

Lectures incorporating practical examples. Project work on chosen datasets.

Course materials

Specified at the start of the course.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level


Term no.: 1
Teaching semester:  AUTUMN 2023

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Chemometrics
  • Signal Processing
  • Multivariate Image Analysis
  • Design of Experiments
  • Engineering Cybernetics
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Engineering Cybernetics


Examination arrangement: Oral examination and work

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Oral examination 70/100 D
Room Building Number of candidates
Autumn ORD Work 30/100
Room Building Number of candidates
Spring ORD Oral examination 70/100 D
Room Building Number of candidates
Spring ORD Work 30/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU