Machine Learning and AI in the Life Sciences: Methods and Applications
Tim Conrad, Christoph Tycowicz
Additional information / Pre-requisites
Prerequisites:
Attended the Statistics course from the Master in Bioinformatics FU (or equivalent)
Comments
This course introduces key machine learning and AI techniques with a strong focus on their applications in the life sciences. You will learn how to analyze complex biomedical data, from pre-processing to model selection and interpretation, using techniques such as dimensionality reduction, feature selection, supervised and unsupervised learning, visual analysis, and deep learning.
Through hands-on projects based on real-world datasets, you will gain practical experience in selecting and applying the right methods to tackle key challenges in the life sciences and bioinformatics, such as disease prediction, biomarker discovery, and patient (status) classification. These projects will reinforce your learning while also helping you develop both technical expertise and scientific communication skills through presentations.
By the end of the course, you will be able to process and model life science data, evaluate AI-driven insights, and effectively communicate your findings. Prior experience in programming (e.g., in R, Python, Java, or C/C++) is highly recommended.
close13 Class schedule
Regular appointments