course-details-portlet

TKJ4175 - Chemometrics

About

Examination arrangement

Examination arrangement: Written examination
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Written examination 100/100 4 hours D

Course content

The course is an introduction to chemometric methods and data analysis with emphasis on applications for chemistry, biotechnology, process chemistry, material science, and physics.
The course covers methods for the design of experiments, preprocessing and modeling of measured data to extract useful information from possibly large data sets, and use this for supporting decisions. Specifically, the following themes are covered: simple regression (the least squared method and polynomial regression), experimental design (full and fractional factorial design), preprocessing (normalization, auto-scaling, Fourier filtering, Savitsky-Golay numerical differentiation, convolution), latent variable methods (principal component analysis/singular value decomposition, principal component regression, partial least squares regression, validation of models (use of test sets, cross-validation, bootstrap and y-randomisation), cluster analysis (hierarchical cluster analysis, k-means cluster analysis and "gap" statistic), classification (random forest, CART and k-nearest neighbours), and an introduction to machine learning techniques for the themes classification, regression and cluster analysis will be given.

Learning outcome

After completing the course, the student should be able to:
(1) Set up, analyse and interpret results from an experimental design
(2) Choose what type of preprocessing methods are necessary in different situations
(3) Make use of unsupervised methods such as PCA and cluster analysis to analyse and interpret multivariate data sets
(4) Make use of regression methods such as PLS and PCR to analyse, interpret and predict multivariate data sets
(5) Make use of validation methods to check the predictive ability of various models
(6) Be able to explain the strengths and weaknesses of both supervised and unsupervised methods

Learning methods and activities

Lectures. Computer exercises and calculating exercises.

Further on evaluation

If there is a re-sit exam, the examination form may be changed from written to oral.

Course materials

Is given in the beginning of the course.

Credit reductions

Course code Reduction From To
SIK3049 7.5
KJ8175 7.5 01.09.2015
More on the course

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  SPRING 2021

No.of lecture hours: 2
Lab hours: 4
No.of specialization hours: 6

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Analytical Chemistry
  • Chemometrics
  • Physical Chemistry
  • Chemistry
  • Technological subjects
Contact information

Department with academic responsibility
Department of Chemistry

Phone:

Examination

Examination arrangement: Written examination

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Spring ORD Written examination 100/100 D INSPERA
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.
Examination

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

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