CourseSpecifications
Valid as from the academic year 2024-2025

Machine Learning (C003758)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 1)
EnglishGent

Lecturers in academic year 2024-2025
Saeys, Yvan

lectureseminar

WE02

lecturer-in-charge

Offered in the following programmes in 2024-2025
Master of Science in Teaching in Science and Technology(main subject Computer Science)
Master of Science in Teaching in Science and Technology(main subject Mathematics)
Master of Science in Teaching in Science and Technology(main subject Physics andAstronomy)
Master of Science in Computer Science
Master of Science in Mathematics
Master of Science in Physics and Astronomy
Master of Science in Physics and Astronomy
Exchange Programme in Computer Science (master's level)
Exchange Programme in Mathematics (master's level)
Exchange Programme in Physics and Astronomy (Master's Level)

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Teaching languages
EnglishKeywords
Machine learning, gesuperviseerd leren (classificatie en regressie), ongesuperviseerd leren(clustering), dimensionaliteitsreductietechnieken
Position of the course
Machine learning technieken stellen ons in staat om automatisch modellen op te stellen op
basis van voorbeelddata. Deze technieken worden in onze huidige samenleving succesvol
gebruik om allerlei taken op te lossen, en zijn een essentiele basisvaardigheid van de moderne
data-wetenschapper.
Contents• Types machine learning technieken• De bias-variance tradeoff• Performantie-evaluatie (cross-validatie, area under ROC curve)• Gesuperviseerd leren• • Fisher LDA• • Support Vector machines• • Neurale netwerken en deep learning• • Probabilistische modellen• • K-Nearest Neighbours• Ongesuperviseerd leren• • Hierarchische clustering• • K-Means• • Density-gebaseerd clusteren• • Self-Organizing maps
• • Gaussian mixture models en EM(Approved)1• Bayesiaanse netwerken en Hidden Markov models• Semi-supervised learning• Dimensionaliteitsreductietechnieken• • Het probleem van overfitting, the curse of dimensionality• • Feature selectie• • Feature transformatie• Toepassingen van Machine Learning
Initial competences
Een goed begrip van datastructuren en algoritmen, een goede kennis van de basis van
probabiliteitstheorie en basisprogrammeervaardigheden.
Final competences
1 Explain the differences among the three main styles of learning: supervised, reinforcement,
1 and unsupervised. [Familiarity]
2 Implement simple algorithms for supervised learning, reinforcement learning, and1 unsupervised learning. [Usage]
3 Determine which of the three learning styles is appropriate to a particular problem domain.
1 [Usage]
4 Compare and contrast each of the following techniques, providing examples of when each
1 strategy is superior: decision trees, neural networks, and belief networks. [Assessment]
5 Evaluate the performance of a simple learning system on a real-world dataset. [Assessment]
6 Characterize the state of the art in learning theory, including its achievements and its
1 shortcomings. [Familiarity]
7 Explain the problem of overfitting, along with techniques for detecting and managing the1 problem. [Usage]
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
Study material
Type: Slides
Name: Course slides, tutorials and papers'
Indicative price: Free or paid by faculty
Optional: noReferences
Pattern Classification, 2nd Edition Richard O. Duda, Peter E. Hart, David G. Stork ISBN: 978-0471-05669-0
The Elements of Statistical Learning: Data Mining, Inference and Prediction (2nd edition)
Trevoer Hastie, Robert Tibshirani and Jerome Friedman
Course content-related study coaching
Persoonlijk contact met de lesgever, via e-mail of op afspraak.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment with open-ended questions
Examination methods in case of periodic assessment during the second examination period
Written assessment with open-ended questions
Examination methods in case of permanent assessment
Oral assessment, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Calculation of the examination markNiet-periodegebonden evaluatie: groepswerk (project) (40%) + periodegebonden: examen(60%). Om te kunnen slagen voor het opleidingsonderdeel moet een student minstens 10/20(Approved)2behalen voor de niet-periodegebonden evaluatie. Is aan deze voorwaarde niet voldaan, dan
kan een student niet meer dan 8/20 halen voor dit vak.
Indien niet geslaagd voor de niet-periodegebonden evaluatie, kan de student het project verderafwerken voor de 2e zittijd.

(Approved)3