Statistical Learning Methods with R
Introduction to R (statistical software); statistical models and evaluation with R; regression simple and multiple; logistic regression; k-nearest neighbors; linear discriminant analysis (LDA); model evaluation, variable selection and regularization; resampling approaches & evaluation; support vector machines (SVM); boosting; unsupervised approaches (principal component analysis, multidimensional scaling);
The final mark is based on both a final written exam and the results of the practical exercises.
T3 – Advanced Information Processing |
T6 – Data Science
The main objectives of this course is to introduce the students to the various techniques coming mainly for the statistical domain in the machine learning paradigm. The main objectives of the course are the followings:
Practical exercises will complete the theoretical presentation.
Jacques Savoy |
The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_2165773.html.
Schedules and Rooms
|Schedule||Wednesday, 14:15 - 18:00|