6 ECTS credits
150 u studietijd
Aanbieding 1 met studiegidsnummer 4013489ENW voor werkstudenten in het 2e semester met een verdiepend master niveau.
In this course, we Introduce the basics of Machine Learning from a statistical perspective. The focus of this course is on supervised learning, but other learning paradigms are also studied. The following topics will be addressed:
1. The Learning Problem - 2. Is Learning Feasible? - 3. The Linear Model - 4. Error and Noise - 5. Training versus Testing - 6. Theory of Generalization - 7. The Vapnik-Chervonenkis Dimension - 8. Bias-Variance Tradeoff - 9. Neural Networks - 10. Overfitting - 11. Regularization - 12. Validation - 13. Support Vector Machines - 14. Kernel Methods - 15. Bayesian learning - 16. Reinforcement learning
Expected background knowledge: For this course, we expect the students to have a decent knowledge of statistics, probability theory, calculus and linear algebra.
Course materials: The course is based on Y. Abu-Mostafa, M. Magdon-Ismail and H.-T. Lin "Learning from Data". We use the following study material: Scientific papers, course slides, book “Learning from data” (Y. Abu-Mostafa) and Canvas notes
The course introduces the student to the basics of Machine Learning from a statistical perspective. The student has to be able to 1) understand the basic ideas behind these techniques, 2) implement these techniques using the Python ecosystem 3) apply these techniques to simple problems, and 4) evaluate their performance.
• Knowledge and insight: After successful completion of the course the student should have insight into which problems can benefit from machine learning techniques and how to apply these techniques to the problem at hand. The student should also have insight in methodological issues involved.
• Use of knowledge and insight: The student should be able to apply machine learning techniques and to tune the parameters of the chosen algorithm. The use of the Python ecosystem should enable the student to write programs to solve problems.
• Judgement ability: The student should be able to judge the qualities of the different machine learning techniques and their results on the problem at hand.
• Communication: The student should be able to communicate with experts about machine learning problems. The student should also be able to report and to present the results of his or her experiments to both specialists and non-specialists.
De beoordeling bestaat uit volgende opdrachtcategorieën:
Examen Schriftelijk bepaalt 50% van het eindcijfer
Examen Praktijk bepaalt 50% van het eindcijfer
Binnen de categorie Examen Schriftelijk dient men volgende opdrachten af te werken:
Binnen de categorie Examen Praktijk dient men volgende opdrachten af te werken:
Written exam: 50% of the final grade.
Practical project: 50% of the final grade.
For the practical project, the students get one assignment that entails a set of research questions, that they can solve in groups of two students. The final exam for this project consists of giving an oral presentation of the solutions to these assignments.
To be eligible to take part in the written exam, the student is expected to register for the project.
Deze aanbieding maakt deel uit van de volgende studieplannen:
Master in de ingenieurswetenschappen: toegepaste computerwetenschappen: Standaard traject
Master in de toegepaste informatica: Artificiële intelligentie
Master of Applied Sciences and Engineering: Applied Computer Science: Standaard traject (enkel aangeboden in het Engels)
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Artificiële Intelligentie
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Multimedia
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Software Languages and Software Engineering
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Data Management en Analytics
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (enkel aangeboden in het Engels)
Educatieve master in de wetenschappen en technologie: computerwetenschappen (120 ECTS, Etterbeek)