Statistical Learning

Statistical learning provides a probabilistic and statistical understanding of topics in machine learning.

The overarching goal of the course is to develop methods for estimating (or `learning') an unknown function from data or making predictions for unseen function outputs. The course aims to empower the student to make a justified decision in adopting machine learning approaches for practical problems and even design their own machine learning methodology using sound mathematical principles. This is achieved by studying key ideas, models, algorithms and theories related to the subject of statistical learning.

In the first half of the course a collection of essential models, algorithms and techniques is introduced. In the second half of the course we adopt a Bayesian/hierarchical models perspective on machine learning and study associated methods for analysing models and developing computational methodology.

The final grade of the course consists of the following components:

* Practical assignments during the course (25%)

* Final written exam (75%)

Final grade calculation: (0.25 * practical assignments + 0.75 * final written exam)

Note: For both components, a sufficient grade (5.8) is required.

Repair possibilities:

In case of an insufficient result, repair opportunities may be offered in accordance with TER Implementation Regulations Art 5, sub 5.

* Assignments: resubmit deliverable

* Written exam: written resit

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4).