Postgraduate course

Statistical Learning

Semester of Instruction


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.

Recommended Previous Knowledge

STAT210, at least STAT111

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

  • Type of assessment: Oral examination

    Withdrawal deadline