Course code FYS301

FYS301 Spectroscopy of Biological Materials and Data Analysis

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Showing course contents for the educational year 2022 - 2023 .

Course responsible: Achim Kohler
Teachers: Johanne Heitmann Solheim, Maren Anna Brandsrud, Volha Shapaval
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
25
Teaching exam periods:
This course starts in June block and has teaching in June block and Autumn parallel. This course has evaluation in Autumn parallel. The course is taught early.
Course frequency: Yearly
First time: Study year 2022-2023
Preferential right:
Master students in photonics have priority.
Course contents:

The course is taught in June and in the following autumn semester. The first part of the course is a part of the BioSpec Summer School. Pre-courses need to be delivered before the start of the summer school. See  https://www.nmbu.no/en/faculty/realtek/research/groups/biospectroscopy/summerschool for teaching period. In the autumn parallel, an individual study on infrared spectroscopy will be performed by all students. The study in the autumn semester will be supported by seminars. 

For the BioSpec Summer School, lectures and tutorials will be evenly spread throughout the teaching period. The course covers the principles of spectroscopy and data science for analysis of data from biophotonics. It gives an introduction to biological data obtained with analytical methods, with a special focus on spectroscopy. The course contains training in data analysis through tutorials and working as an expert in a multidisciplinary team. Furthermore, special focus will be given on the development of personal presentation skills. 

The course covers:  

  • Spectroscopic techniques 
  • Analytical methods in biology 
  • Principal Component Analysis 
  • Basic methods of pre-processing for spectroscopic data 
  • Advanced pre-processing methods such as Mie correction and fringes correction 
  • Multivariate methods for clustering 
  • Basic regression 
  • Machine learning methods for regression and classification 
  • Deep learning methods 
  • Data analysis of hyperspectral images 
  • Introduction to the machine learning software platform Orange/Quasar 
Learning outcome:
  • Gain multidisciplinary knowledge in biology, spectroscopy and data analysis.
  • Improve theoretical and practical knowledge in data analysis of high-dimensional biological data including hyperspectral imaging data.
  • Acquire skills for working in an international and multidisciplinary team.
  • Acquire presentation skills through presenting the results of the course project.
  • Improve theoretical and practical knowledge within biophotonics and gain an overview of the analytical methods used in the field.
  • Improve theoretical knowledge in the interaction between light and biological matter.
  • Get insight into how the discipline has evolved and impacted society through invited lectures about applications of biophotoncis and data science.
  • Be able to carry out interdisciplinary analyses with colleagues from other academic fields.
  • Acquire skills to analyze and critically evaluate various sources of information and use them to structure and formulate scholarly arguments.
  • Improve communication skills about academic issues, analyses and conclusions in the field, both with specialists and the general public.
  • Get an overview over different spectroscopic techniques
  • Get practical experience with infrared spectroscopy
  • Acquire presentation skills through presenting the results of individual work
Learning activities:
PRE-COURSE: Lectures and exercises.LECTURES: The lectures present the central ideas, theory and examples.TUTORIALS: The tutorials contain exercises for hands on training of data analytical tools with relevant data from biology and spectroscopy. The assistant teachers take part in discussions. PRESENTATION: Students present in groups the outcome of the tutorials where they have worked on one relevant data set from biology and where they under supervision answer research questions related to the data. INDEPENDENT STUDY IN AN TEAM OF EXPERTS: Students work as experts in an interdisciplinary team where they discuss and analyse a data set. They perform a limited research project under supervision with colleagues from other academic fields. The result is presented and evaluated. INDIVIDUAL CASE STUDY: Students will work on an individual study with use of infrared spectroscopy.
Teaching support:
Students will be able to meet the lecturer and assistant teachers throughout the lectures, tutorials and seminars.
Syllabus:
List will be handed out during the first lecture.
Prerequisites:
Students need to have a bachelor’s degree in natural sciences.
Recommended prerequisites:
Students with a physics, chemistry, biology background or similar background will be eligible.
Mandatory activity:
Lectures (pre-course and course week), tutorials, group work, seminars and individual case study.
Assessment:

(1) Pre-course exercises need to be passed

(2) Final presentation of tutorials and presentations from work done in Expert in a team at the Summer School

(3) Individual report should be delivered on selected modules from Summer School

(4) Presentation of individual study

The final grade will be based on the individual grades given for (2)-(4).  

Nominal workload:
250 hours including structured teaching, pre-courses, and individual case study.
Entrance requirements:
Bachelor in natural sciences
Reduction of credits:
Some years FYS401 with 10 ECTS. Contact course responsible for information.
Type of course:
The course contains lectures and tutorials in the June block.
Note:
-The course needs a lecture hall, and rooms for tutorials during the June block. The rooms for tutorials need to be close to the lecture hall.
Examiner:
The examiner will evaluate the course, participate in the presentations and ask questions. 
Examination details: :