English title:
Machine learning and pattern recognition for bioinformatics
Course ID:
790384
ECTS credits:
4,0
Title in native language:
Machine learning and pattern recognition for bioinformatics (in Eng.)
Machine learning and pattern recognition for bioinformatics (in Eng.)
Term Semester:
Spring/Summer
Instruction language(s):
English
Course content:
Introduction to machine learning and pattern recognition for data analysis in bioinformatics.
1) Theory:
1.1) Brief introduction to Python 3, numpy und pandas.
1.2) Classification of problems and optimal choice of analysis methods.
1.3) Supervised learning for modeling of continuous and discrete observations. Derivation of relations between machine learning methods and statistical models.
1.4) Unsupervised learning and exploratory data analysis.
1.5) Methods used in machine learning for model diagnosis and model selection.
1.6) Discussion of selected machine learning and pattern recognition algorithms from the scikit.learn library in bioinformatics use cases.
2) Practical part:
Application of the theoretical skills to biological data analysis problems using the publicly available Python 3 libraries numpy, pandas and scikit.learn.
1) Theory:
1.1) Brief introduction to Python 3, numpy und pandas.
1.2) Classification of problems and optimal choice of analysis methods.
1.3) Supervised learning for modeling of continuous and discrete observations. Derivation of relations between machine learning methods and statistical models.
1.4) Unsupervised learning and exploratory data analysis.
1.5) Methods used in machine learning for model diagnosis and model selection.
1.6) Discussion of selected machine learning and pattern recognition algorithms from the scikit.learn library in bioinformatics use cases.
2) Practical part:
Application of the theoretical skills to biological data analysis problems using the publicly available Python 3 libraries numpy, pandas and scikit.learn.
Expected previous knowledge:
Skills in Mathematics and Statistics which are provided in the compulsory courses in the Biotechnology bachelor and master curricula.
Learning outcomes:
Successful completion of this course enables students to independently analyze bio(techno)logical data sets with machine learning methods. Course participants will after a successful completion understand the mathematical and statistical background of machine learning algorithms. Successful students will know ho to apply important scikit.learn algorithms and be able to script solutions for bioinformatics data analysis problems in Python 3. Students will furthermore have acquired skills which allow them to assess machine learning derived analysis results quantitatively. The theoretical skills which students acquire during this course allow them to critically assess published methods and to select optimally suited strategies for their future bioinformatics data analysis challenges.
Teaching and learning methods:
The lecture consists of a theory and a practical part. The theory part consists of four half day lecturing units whereas the practical part consists of a two day introduction and a three day ML boot camp.
The 4.0 ECTS of this lecture result from a workload which is structured as follows:
1) Face to face lecturing amounts to 1.5 ECTS
2) Completing the theoretical homework amounts to 0.5 ECTS
3) Completion of introductory Python programming exercises ammounts to 0.5 ECTS
4) Completion of the comprehensive ML boot camp challenges amounts to 1.5 ECTS
The 4.0 ECTS of this lecture result from a workload which is structured as follows:
1) Face to face lecturing amounts to 1.5 ECTS
2) Completing the theoretical homework amounts to 0.5 ECTS
3) Completion of introductory Python programming exercises ammounts to 0.5 ECTS
4) Completion of the comprehensive ML boot camp challenges amounts to 1.5 ECTS
Exam method:
This course has an innate exam character. Students are assessed on their performance in the following subtasks which contribute according to the respective scores.
1) 2 theoretical homework assignments each awarded with 5 points (teamwork with presentation)
2) 5 introductory programming tasks in Python each awarded with 2 points (individual assignments and presentation of results)
3) 3 comprehensive ML boot camp challenges each awarded with 10 points (task completion in teams of at most two students).
Grading scale: > 44 points: first grade (1); > 38 points: second grade (2); >31 points: grade three (3); >24 points: grade four (4);
The awarded grades will be assigned in a final discussion session which takes place about a month after the ML boot camp. Students may improve the proposed grade by opting for an additional verbal exam.
1) 2 theoretical homework assignments each awarded with 5 points (teamwork with presentation)
2) 5 introductory programming tasks in Python each awarded with 2 points (individual assignments and presentation of results)
3) 3 comprehensive ML boot camp challenges each awarded with 10 points (task completion in teams of at most two students).
Grading scale: > 44 points: first grade (1); > 38 points: second grade (2); >31 points: grade three (3); >24 points: grade four (4);
The awarded grades will be assigned in a final discussion session which takes place about a month after the ML boot camp. Students may improve the proposed grade by opting for an additional verbal exam.
Organisation: University of Natural Resources and Life Sciences Vienna
Country:
Austria
Acronym:
BOKU
ERASMUS+ code:
A WIEN03
Teaching period summer semester:
22. Feb 2021 - 30. Sep 2021
Teaching period winter semester:
13. Oct 2020 - 21. Feb 2021
University website: