Machine Learning
Teacher
Professor: Ann Nowé
Goal
Knowledge and insight
The student is acquainted with a range of basic learning algorithms. The student is capable of applying evaluation techniques to estimate the performance of the obtained model and to calculate the performance of an algorithm given a concrete application context using computational learning theory.
The use of knowledge and insight
He is able to choose the appropriate techniques given a concrete learning problem, to apply them correctly and to evaluate the obtained results.
Judgement ability
The student must be able to devise and sustain arguments in favor of or against some choice of learning technique for a given problem.
Communication
He/she can motivate the chosen approach to specialist and non specialists.
Skills
Students have obtained the skills to autonomously program, analyse, and apply learning techniques to a wide variety of problems.
Content
- Concept Learning
- Decision Tree Learning
- Bayesian Learning
- Instance-Based Learning
- Inductive Logic Programming
- Evaluating Hypotheses
- Computational Learning Theory
- Reinforcement Learning
Course Material
Machine learning, Tom Mitchell. This book can be bought from Infogroep at a good price.
The slides can be downloaded from Tom Mitchell's website.
You can find additional material, such as exercises, on the PointCarré website of this course: VUB12173.
Exam
The exam is partly oral, partly written and open book.
There will be one project, a case study. This work is mandatory and should be handed in before the exam session in June. The exact deadline will be announced later. The result will be taken into account for the final score.
