LLEK10246U Advanced Chemometrics
Volume 2013/2014
Education
MSc Programme in Food Science
and Technology
MSc Programme in Biology -Biotechnology
MSc Programme in Agriculture
MSc Programme in Biology -Biotechnology
MSc Programme in Agriculture
Content
Basic chemometric methods like PCA and
PLS are useful tools in data analysis but in many data analytical
problems more advanced 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, calibration transfer methods, non-linear regression and multi-way methods.
Computer exercises on real data using commercial software are an integrated part of the course.
It is expected that the student have competences corresponding to the course Exploratory Data Analysis / Chemometrics.
The methods studied in this course will be selected from these main topics: Data preprocessing methods, variable selection methods, clustering and classification techniques, calibration transfer methods, non-linear regression and multi-way methods.
Computer exercises on real data using commercial software are an integrated part of the course.
It is expected that the student have competences corresponding to the course Exploratory Data Analysis / Chemometrics.
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:
Knowledge:
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
Skills:
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.
Competences:
Discuss advantages and drawbacks of advanced methods
After completing the course the student should be able to:
Knowledge:
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
Skills:
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.
Competences:
Discuss advantages and drawbacks of advanced methods
Literature
See course web-site.
Scientific papers, book chapters and course notes.
Scientific papers, book chapters and course notes.
Academic qualifications
Must have experience with
PCA and PLS regression
Teaching and learning 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 as suitable to illustrate the
taught methods. The results are presented in written reports which
is orally defended at the end of the course.
Workload
- Category
- Hours
- Exam
- 1
- Lectures
- 69
- Preparation
- 70
- Project work
- 66
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 20 min.---
- Exam registration requirements
- All reports must be approved before exam
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
More than one internal examiner
Criteria for exam assesment
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 in project reports and in the examination requirements 100%
Weight: Oral examination in project reports and in the examination requirements 100%
Course information
- Language
- English
- Course code
- LLEK10246U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- A
- Course capacity
- 25 students
- Continuing and further education
- Study board
- Study Board of Food, Human Nutrition and Sports
Contracting department
- Department of Food Science
Course responsibles
- Rasmus Bro (2-75654369727267316e7831676e)
Saved on the
07-05-2013