CourseSpecifications
Valid as from the academic year 2024-2025

Machine Learning Methods for Biomedical Data (D012554)

Course size
Credits 5.0(nominal values; actual values may depend on programme)
Study time 150 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
Degroeve, Svenpeer teaching
seminarlectureGE31

lecturer-in-charge

Offered in the following programmes in 2024-2025
Master of Science in Biomedical Sciences

crdts
5offeringA

Teaching languages
EnglishKeywords
machine learning, biomedical data analysis, data visualization, AI
Position of the course
Prediction models learned from biomedical data constitute an important part of biomedicalresearch. Increasingly frequently they enter the clinical environment to, among other things,
assist with diagnosis (such as medical image interpretation or gene expression classification).
In this course it is taught, in the form of colleges, microteaching and associated practicals, howalgorithms can learn from data and how to program that in Python.
The emphasis is mainly on the practical application of machine learning. In a machine learningcompetition, students attempt to program the best model for a specific task.
The course is offered in the first Master Biomedical Sciences and is linked to the Advanced
Bioinformatics from the first Master Biomedical Sciences.
Contents• linear regression, logistic regression• random forest, gradient boosting• deep neural networks• computer vision: convolutional neural networks• sequence learning: recurrent neural networks• sequence learning: transformer architectures• generative models: generative adversarial network (GAN)• generative models: generative pre-training transformer (GPT)• model building and evaluation with scikit-learn (Python)Initial competences
Successfully have completed the Biomedical Analysis I, Biomedical Analysis II, Statistics, Data
Analysis I, Informatics I, and Informatics II courses, or have acquired the required competences
for these courses through another means.
Successfully have completed the bachelor in Biomedical Sciences or have acquired theacquired competences of this bachelor curriculum through another means.
Final competences
1 Gain insight in the practical treatment and processing of large amounts of data, obtained
1 from the broader life sciences.
2 The student is capable of formulating data analysis tasks.
3 Gain insight in the available machine learning algorithms.
(Approved)1

4 Gain insight into the most important parameters in machine learning analyses.
5 The student is capable of clearly visualizing the results obtained from a data analysis.
6 The student is capable of indepdently starting and successfully completing a machine
1 learning based data analysis.
7 The student is capable of interacting at high level with data analysis specialists.
8 The student is capable of writing machine learning applications in Python.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture, Peer teaching
Study material
NoneReferences
Course content-related study coaching
Support during and after colleges and practicals, and via Minerva
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment with multiple-choice questions, Written assessment with open-ended questions
Examination methods in case of periodic assessment during the second examination period
Written assessment with multiple-choice questions, Written assessment with open-ended questions
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
Continuous evaluation:• Jupyter notebook project (15%)• Microteaching: teach other students about a machine learning method not discussed during• the lectures (15%)• 2 page report about the obtained Kaggle contest results (20%)
Period-specific evaluation
Closed-book, written exam on 50% of the total score, concerning the subject matter describedin the course material and the colleges. The exam consists of both open and MCQ.
Calculation of the examination markContinuous evaluation• Jupyter notebook project (15%)• Microteaching: teach other students about a machine learning method not discussed during• the lectures (15%)• 2 page report about the obtained Kaggle contest results (20%)
Period-specific evaluation
Written exam on 50% of the total score.

(Approved)2