TMPS36 · 6 credits · Advanced level (A1F) · Teknisk biologi
Machine Learning in Bioprocess Engineering
Active · next start HT 2026
100%
passed the latest regular sitting
LAB1 · 17 Dec 2025 · 38 students
100%
passed the regular sittings, all sittings over the last 5 years
LAB1 · 62 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 2 programmes
Hand-in or lab
LAB1 Laboratory Assignments
- G
Main rounds
100% passed · 62 students
- 100%38
- 100%24
Later rounds
100% passed · 18 students
- 100%18
- Pass a later round: 100% (18 students), compared with 100% (62 students) on the ordinary exam
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Written report
- 5
Main rounds
100% passed · 80 students
- 100%38
- 100%24
- 100%18
Later rounds
No sittings with at least 5 students.
- Get 5: 100% (80 students)
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
About the course
Aim
After the course the student should be able to:
- Describe what is data and identify different data sources and data kinds in bio-processes
- Analyze different bio-processes datasets using supporting digital tools
- Explain the best practices for collecting and organizing data into a data set
- Implement one data set
- Chose a method to model a bio-process using machine learning algorithms considering the desired outcomes of the model
- Model bio-process using machine learning algorithm and evaluate the implemented model
Content
The lectures and labs will cover the following topics:
- Data, AI and their relevance in the biotech sector and in industry in general
- What kind of Data do Bio-processes generate
- How to digitally collect, organize and visualize bio-process data.
- Introduction to selected AI methods and techniques applicable to Bio-processes
- Application of AI methods and techniques for modeling of bio-processes
- Examiner
- Johan Persson
- Scheduled hours
- 32 h
- Main field of study
- Teknisk biologi