Statistical learning: from parametric to nonparametric models - WMM9AM78

Goal(s)

This course is related to mathematical and statistical methods which are very used in supervised learning.

It contains two parts.

In the first part, we will focus on parametric modeling. Starting with the classical linear regression, we will describe several families of estimators that work when considering high-dimensional data, where the classical least square estimator does not work. Model selection and model assessment will particularly be described.

In the second part, we shall focus on nonparametric methods. We will present several tools and ingredients to predict the future value of a variable. We shall focus on methods for non parametric regression from independent to correlated training dataset. We shall also study some methods to avoid the overfitting in supervised learning.

This course will be followed by practical sessions with the R software.

Responsible(s)

Anatoli IOUDITSKI, Emilie DEVIJVER

Content(s)

Introduction.
Penalized linear methods for regression and classification.
Non linear methods for regression.
Cross Validation.

basic probability statistical inference, linear model.

Test

Evaluation : 50% of Projet (évaluation en continu et sur le rendu) and 50% of Examen écrit (2h00)

Resit : Examen écrit (2h00)

Assessment = written / project
Final exam = written (2h)
Re take = written session 2 (2h)

The exam is given in english only

Calendar

The course exists in the following branches:

• Curriculum - Master in Computer Science - Semester 9 (this course is given in english only)
• Curriculum - Master in Applied Mathematics - Semester 9 (this course is given in english only)

Additional Information

Course ID : WMM9AM78
Course language(s):

You can find this course among all other courses.