Bi9680enc Artificial Intelligence in Biology, Chemistry, and Bioengineering - practice

Faculty of Science
Autumn 2024
Extent and Intensity
0/1/0. 1 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: k (colloquium).
In-person direct teaching
Teacher(s)
prof. Mgr. Jiří Damborský, Dr. (lecturer)
Stanislav Mazurenko, PhD (lecturer)
Faraneh Haddadi (seminar tutor)
Ing. Pavel Kohout (seminar tutor)
Bc. Dávid Lacko (seminar tutor)
Bc. Karen Pailozian (seminar tutor)
Ing. Jan Velecký (seminar tutor)
Providers of Specific teaching support
Zbyněk Cincibus (přepisovatel)
Guaranteed by
prof. Mgr. Jiří Damborský, Dr.
Department of Experimental Biology – Biology Section – Faculty of Science
Contact Person: Stanislav Mazurenko, PhD
Supplier department: Department of Experimental Biology – Biology Section – Faculty of Science
Timetable
Thu 12:00–13:50 B09/316
Prerequisites
Bi9680en AI in Bioengineering || NOW( Bi9680en AI in Bioengineering )
No prior experience in programming is expected.
Course Enrolment Limitations
The course is offered to students of any study field.
The capacity limit for the course is 40 student(s).
Current registration and enrolment status: enrolled: 18/40, only registered: 1/40, only registered with preference (fields directly associated with the programme): 0/40
Course objectives
The main objective of this course is to provide students with hands-on experience in programming simple examples of machine-learning-based predictors in Python. The practicals will follow the theory presented during the lectures of Bi9680en. We will cover the basics of programming, some useful libraries for data analysis and machine learning, and create simple predictors for biologically-relevant data. We will also demonstrate how to use existing predictive models.
Learning outcomes
After completing the course, a student will be able to:
- understand the basics of the code flow;
- operate with basic types of variables, functions, if-conditions, and for-loops;
- operate the Spyder editor;
- implement a simple machine learning workflow in Python;
- train and validate simple predictors;
- reproduce a simple machine-learning study.
Syllabus
  • Introduction to programming in Python – the first code;
  • Booleans, if-conditions, for-loops, basic functions;
  • Introduction to NumPy and pandas libraries;
  • Hierarchical clustering, plots;
  • Decision trees, regression;
  • Cross-validation, learning curves;
  • Case study in bioengineering;
  • Use of trained ML models.
Teaching methods
8 practical sessions in the computer lab, homework
Assessment methods
In order to pass, a student must complete a series of short homework assignments.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2024/Bi9680enc