Advanced Chemometrics and Machine Learning

Course content

Basic chemometric methods like PCA and PLS are useful tools in data analysis but in many data analytical problems more advanced machine learning methods are necessary to solve the problems.

The methods studied in this course will be selected from these main topics: Data preprocessing methods, variable selection methods, clustering and classification techniques, non-linear regression and multi-way methods.

Computer exercises on real data using commercial software are an integrated part of the course.




MSc Programme in Biotechnology
MSc Programme in Environmental Science
MSc Programme in Food Science and Technology

Learning outcome

The course introduces advanced chemometric methods and their use on different kinds of multivariate data of relevance for research and development.

After completing the course the student should be able to:


  • Summarize basic chemometric methods
  • Describe advanced chemometric methods for multivariate (clustering, classification and regression) data analysis
  • Describe advanced techniques for data pre-preprocessing
  • Describe advanced methods for variable selection.


  • Apply theory on real life data analytical cases
  • Apply commercial software for data analysis
  • Report in writing a full data analysis of a given problem including all aspects presented under Knowledge.


  • Discuss advantages and drawbacks of advanced methods.

The students will be introduced to the theory through lectures and seminars. The students will work on data analytical problems using the taught methods and software to analyse data. The students can bring their own data analytical problems to work on; this requires that the course teachers consider the data suitable for illustrating the taught methods. The results are presented in written reports which is orally defended at the end of the course.

See Absalon for specific course literature

Competences in the field of Exploratory Data Analysis / Chemometrics (experience with PCA and PLS regression) is highly recommended. The course coordinator should be contacted in case of doubt about sufficient prerequisites.

Academic qualifications equivalent to a BSc degree is recommended.

The course is identical to the discontinued course LLEK10246U Advanced Chemometrics. Therefore you cannot register for NFOK21000U - Advanced Chemometrics and Machine Learning, if you have already passed LLEK10246U Advanced Chemometrics.
If you are registered with examination attempts in LLEK10246U Advanced Chemometrics without having passed the course, you have to use your last examination attempts to pass the exam in NFOK21000U - Advanced Chemometrics and Machine Learning. You have a total of three examination attempts

Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
7,5 ECTS
Type of assessment
Oral examination, 20 min
Type of assessment details
The students will hand in a number of written reports in due time before the oral examination. At the oral examination, the student will be examined in the reports as well as the curriculum.
Weight: Oral examination, 100%
Exam registration requirements

All reports must be approved before the exam.

All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Non-approved project reports must be revised and submitted two weeks before the re-examination, otherwise same as ordinary exam.

Criteria for exam assessment

See Learning Outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 69
  • Preparation
  • 110
  • Project work
  • 26
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 3
The number of seats may be reduced in the late registration period
Study Board of Food, Human Nutrition and Sports
Contracting department
  • Department of Food Science
Contracting faculty
  • Faculty of Science
Course Coordinators
  • Rasmus Bro   (2-7868466c75756a34717b346a71)
  • Morten Arendt Rasmussen   (7-6f7174766770744268717166306d7730666d)
  • Beatriz Quintanilla Casas   (7-64676376746b7c4268717166306d7730666d)
Saved on the 06-09-2023

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