Student Pages
Postgraduate course

Statistical Learning

Main content

Teaching semester


Objectives and Content

Topics treated in this course include regression, classification, model selection and a certain introduction to machine learning. The student will apply different packages in R.

Learning Outcomes

After completing the course, students should be able to:

  • use nonlinear regression methods such as Spline, Local Regression and Generalized Additive Models
    apply classification methods such as Logistic Regression, Linear Discriminant Analysis
  • know how to use resampling (cross validation, bootstrap) and Model Selection methods to assess and select models and deal with high dimensional data
  • apply Tree-Based Methods such as decision tree, Bagging, Random Forests, Boosting
  • avail Support Vector Machines for resolving classification and regression problem.
  • know unsupervised learning methods such as Principal Components Analysis and Clustering Methods.
  • know about Deep learning and Naive Bayes.

Recommended Previous Knowledge


Compulsory Assignments and Attendance

Two approved compulsory excercises

Forms of Assessment

Oral examinations. Approved compulsory exercises is required to take the exam .

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Exam information