791384 Machine learning and pattern recognition for bioinformatics

Lecture and exercise
Semester hours
Lecturer (assistant)
Offered in
Sommersemester 2018
Languages of instruction


Introduction to machine learning and pattern recognition for data analysis in bioinformatics.
1) Theory:
1.1) Brief introduction to MatLab
1.2) Classification of problems and optimal choice of analysis methods.
1.3) Supervised learning for modeling of continuous and discrete observations. Discussions of the relation of respective machine learning methods with 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) Applications of machine learning and pattern recognition in bioinformatics.

2) Practical part:
Application of the theoretical skills to biological data analysis problems using publicly available MatLab libraries.

Previous knowledge expected

Skills in Mathematics and Statistics which are provided in the compulsory courses in the Biotechnology Bachelor and Master curricula.

Objective (expected results of study and acquired competences)

Successful completion of this course enables students to analyse biological data sets with machine learning methods. Participants will after completion of the course understand the mathematical and statistical background of machine learning algorithms. Participants will in addition be familiar with important machine learning models and algorithms, be able to script solutions of data analysis problems in MatLab and to assess solutions quantitatively.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.