Statistical Learning Methods

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.
 

Learning Outcomes: 

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:

  1. to be able to use and program in R, the statistical language (used to analyze Big Data);
  2. to select, apply and evaluate a learning method using R and to interpret the results;
  3. to select the most appropriate method according to the data, and to evaluate the quality of the fit.

Practical exercises will complete the theoretical presentation.
 

Type: 
Course
Semester: 
A2018
ECTS: 
5
Lecturer: 
Site: 
N
Code: 
32079
62079
Language: 
english
Period: 
weekly
Schedule: 
Wednesday: 8:45 - 12:00
Location: 
UniNE, Unimail
Room: 
B013
Comment: 

First Lecture
The first lecture will take place on Wednesday, 19.09.2018 at 08:45 in UniNE, Unimail, room B104.

ILIAS
The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_1340257.html.

References

  • G. Jones, D. Witten, T. Hasti, R. Tibshirani: An Introduction to Statistical Learning.  With Applications in R.  Springer, 2013.
  • C.M. Bishop:  Pattern Recognition & Machine Learning.  Springer, 2006. 
  • M.J. Crawley:  The R Book.  2nd Ed., Wiley, 2012.