| Responsible Department | Department of Food Science | ||||||||||||||||||
| Earliest Possible Year | MSc. 1 year to MSc. 2 year | ||||||||||||||||||
| Duration | One block | ||||||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||||||
| Level of Course | MSc | ||||||||||||||||||
| Examination | Final Examination written examination and oral examination All aids allowed Description of Examination: The students will hand in a written group report in due time before the oral examination. At the individual oral examination the students will be examined in the report as well as the examination requirements. Weight: Oral examination in project report and in the examination requirements 100% 7-point scale, internal examiner | ||||||||||||||||||
| Organisation of Teaching | Lectures (25%), exercises (25%), colloquia (5%), project work (45%) | ||||||||||||||||||
| Block Placement | Block 2 Week Structure: A | ||||||||||||||||||
| Language of Instruction | English | ||||||||||||||||||
| Optional Prerequisites | 270006 Exploratory Data Analysis / Chemometrics | ||||||||||||||||||
| Restrictions | 50 | ||||||||||||||||||
| Course 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. | |||||||||||||||||||
| Teaching and learning Methods | |||||||||||||||||||
| The students will be introduced to the theory through lectures and seminars. The students will work in groups on a data analytical problem using the taught algorithms and software to analyse the problem. 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 a written report which is orally defended at the end of the course. | |||||||||||||||||||
| 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) and multi-way 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 drawback of advanced methods Present reading material for a group of peers | |||||||||||||||||||
| Course Literature | |||||||||||||||||||
| See course web-site. Scientific papers, book chapters and course notes. | |||||||||||||||||||
| Course Coordinator | |||||||||||||||||||
| Rasmus Bro, rb@life.ku.dk, Department of Food Science/Quality and Technology, Phone: 353-33296 Franciscus Winfried J van der Berg, fb@life.ku.dk, Department of Food Science/Quality and Technology, Phone: 353-33545 | |||||||||||||||||||
| Study Board | |||||||||||||||||||
| Study Committee LSN | |||||||||||||||||||
| Work Load | |||||||||||||||||||
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