BTLW002296 Introduction to algorithms in bioinformatics

Type: Lecture and exercise
Semester hours: 3
Lecturer (assistant): Sandell, Felix Leopold
Organisation: Bioprocess Science and Engineering
Offered in: Wintersemester 2025/26
Languages of instruction: Englisch
Content: This course introduces important algorithms used in computational biology, with attention to both their underlying theoretical concepts and practical applications. Topics include dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), machine learning methods based on decision trees (Random Forest and gradient boosting) and core methods in genomics, including genome mapping and variant calling. Students will learn to implement the basic concepts of these algorithms in Python, building both a solid understanding of the theoretical concepts and the programming skills needed to apply them.
Previous knowledge expected: Basic knowledge in python programming (e.g. Introduction to programming, Programming with Python) and basic Linux knowledge (e.g. Computing skills for biotechnology).
Objective (expected results of study and acquired competences): By the end of the course, students will be able to explain the principles of selected algorithms in computational biology and implement basic versions of them in Python.

You can find more details like the schedule or information about exams on the course-page in BOKUonline.