Statistical Foundations of Machine Learning

6 ECTS credits
150 h study time

Offer 1 with catalog number 4013489ENW for working students in the 2nd semester at a (E) Master - advanced level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Enrollment Requirements

NOTE: registration for this course is only possible for working students. Day students can register for courses whose code ends with an R. At Inschrijven / studentenadministratie@vub.be you must be registered at the VUB as a working student for the current academic year.

Taught in

English

Partnership Agreement

Under agreement for exchange of courses

Faculty

Faculty of Sciences and Bioengineering Sciences

Department

Computer Science

Educational team

Pieter Libin (course titular)

Activities and contact hours

24 contact hours Lecture
24 contact hours Seminar, Exercises or Practicals
36 contact hours Independent or External Form of Study

Course Content

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

Additional info

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

Learning Outcomes

General competencies

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.

Grading

The final grade is composed based on the following categories:
Written Exam determines 50% of the final mark.
Practical Exam determines 50% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

final exam: oral presentation with a relative weight of 1 which comprises 50% of the final mark.

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

Within the Practical Exam category, the following assignments need to be completed:

practical project with a relative weight of 1 which comprises 50% of the final mark.

Note: 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.

Additional info regarding evaluation

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.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context