Course - Multivariate Data Analysis - Advanced Topics - TK8117
TK8117 - Multivariate Data Analysis - Advanced Topics
About
Examination arrangement
Examination arrangement: Oral examination and work
Grade: Passed / Not Passed
| Evaluation | Weighting | Duration | 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.
Recommended previous knowledge
Introduction to multivariate modelling. Good knowledge of linear algebra and basic statistical methods.
Course materials
Specified at the start of the course.
No
Version: 1
Credits:
7.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: AUTUMN 2024
Language of instruction: English, Norwegian
Location: Trondheim
- Chemometrics
- Signal Processing
- Multivariate Image Analysis
- Design of Experiments
- Engineering Cybernetics
Department with academic responsibility
Department of Engineering Cybernetics