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.
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 .
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Type of assessment: Oral examination
- Withdrawal deadline