Hands-on Machine Learning in Life Science with R
Course facts
P000154, Hands-on Machine Learning in Life Science with R, 5.0 Hp
Print syllabus
Syllabus
Finalized by: Galia Zamaratskaia and Ali Moazzami, 2025-10-21
Valid from :
Level
Third cycle
Subject
Mathematical Statistics, Biology
Grading Scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
English
Entry Requirements
- Basic familiarity with R and introductory statistics
Objectives
Course Objectives
This course provides a hands-on approach to machine learning in life sciences, focusing on the practical application of key algorithms using R. Students will gain basic knowledge of supervised and unsupervised models and learn how to apply, evaluate, and interpret these models in real-world biological datasets.
Learning Outcomes
By the end of this course, students will be able to:
- Explain the workflow of supervised and unsupervised machine learning for life‑science data
- Implement decision trees, random forests, SVMs, clustering, PCA, and simple ANNs in R
- Evaluate and tune models (resampling, hyperparameters, performance metrics)
- Interpret results for scientific reporting in agricultural, biological, and medical contexts
- Communicate findings via a short, structured project presentation
Content
Teaching & Assessment Strategy
- Synchronous sessions: 2 per week (Lecture/Discussion and Lab), ~1.5–2 h each
- Assessment: One combined assignment (supervised + unsupervised) and one final project presentation
- Short pre-class video/readings for theory; live time is used for Q&A and hands‑on labs
Delivery: Online (Zoom)
Examination Formats and Requirements for Passing the Course
**Pass criteria:** Submission quality, code correctness/reproducibility, methodological justification, and clarity of interpretation.
Responsible Department/Equivalent
Department of Molecular Sciences