Statistical Foundations of Machine Learning

6 ECTS credits
150 u studietijd

Aanbieding 1 met studiegidsnummer 4013489ENW voor werkstudenten in het 2e semester met een verdiepend master niveau.

Semester

2e semester

Inschrijving onder examencontract

Niet mogelijk

Beoordelingsvoet

Beoordeling (0 tot 20)

2e zittijd mogelijk

Inschrijvingsvereisten

Dit opleidingsonderdeel is enkel toegankelijk voor werkstudenten.

Onderwijstaal

Engels

Onder samenwerkingsakkoord

Onder uitwisselingsakkoord mbt studiedelen

Faculteit

Faculteit Wetenschappen en Bio-ingenieurswetensch.

Verantwoordelijke vakgroep

Computerwetenschappen

Onderwijsteam

Pieter Libin (titularis)

Onderdelen en contacturen

24 contacturen Hoorcollege
24 contacturen Werkcolleges, practica en oefeningen
36 contacturen Zelfstudie en externe werkvormen

Inhoud

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

Bijkomende info

Expected background knowledge: For this course, we expect the students to have a good knowledge of statistics, probability theory, calculus, and linear algebra. Familiarity with these topics is essential to understand the mathematical underpinnings of machine learning algorithms. We assume that students have experience in programming: all exercises and the exam project will be programmed in python.

Course materials: The course is based on the book by Y. Abu-Mostafa, M. Magdon-Ismail and H.-T. Lin: "Learning from Data". We use this course material: Scientific papers, course slides, book “Learning from data” and Canvas notes, lecture videos, WPO assignments and solutions (theoretical and programming exercises).

Leerresultaten

General competences

Introduce the basics of Machine Learning from a statistical perspective. The student has to be able to 1) understand machine learning techniques, 2) formally prove theoretical guarantees about machine learning, 3) implement these techniques in Python, 4) apply these techniques to benchmark and real-world problems, and 5) evaluate the performance of machine learning techniques.

• 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 will gain insight in the studied methodologies and be able to reason about model complexities and learning guarantees.

• 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 python will enable the student to write programs to solve problems. The exercise sessions and practical exam project will challenge students to solve research questions that consider both synthetic and real-world data.

• 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. The practical exam project will challenge students to collaborate with their peers and communicate their results effectively.

Beoordelingsinformatie

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:

written exam met een wegingsfactor 1 en aldus 50% van het totale eindcijfer.

Toelichting: Written exam: 50% of the final grade.

Binnen de categorie Examen Praktijk dient men volgende opdrachten af te werken:

practical project met een wegingsfactor 1 en aldus 50% van het totale eindcijfer.

Toelichting: Practical project: 50% of the final grade.
For the practical project, the students get 4 assignments that they can solve in groups of two. The final exam for this project consists of giving an oral presentation of the solutions to these assignments.

Aanvullende info mbt evaluatie

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 should solve in groups of three students. The final exam for this project consists of writing a short paper and 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.

Toegestane onvoldoende

Kijk in het aanvullend OER van je faculteit na of een toegestane onvoldoende mogelijk is voor dit opleidingsonderdeel.

Academische context