NFOK21000U Advanced Chemometrics and Machine Learning
MSc Programme in Biotechnology
MSc Programme in Environmental Science
MSc Programme in Food Science and Technology
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.
The course introduces advanced chemometric methods and their use
on different kinds of multivariate data of relevance for research
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.
See Absalon for specific course literature
Academic qualifications equivalent to a BSc degree is recommended.
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
- Project work
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- 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 assesment
See Learning Outcome
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 3
- Course capacity
The number of seats may be reduced in the late registration period
- Study Board of Food, Human Nutrition and Sports
- Department of Food Science
- Faculty of Science
- Rasmus Bro (2-7868466c75756a34717b346a71)
- Morten Arendt Rasmussen (7-6f7174766770744268717166306d7730666d)