Five week courses - Machine Learning Methods for Classification and Prediction

Machine Learning Methods for Classification and Prediction

Enter Title


The main objective of the course is to provide students with the skills and abilities to critically judge the possibility of application of certain machine learning methods in a business context, including their advantages and disadvantages.

Detailed course description is available here.


Assistant Professor Tea Šestanović, PhD

Lecturer CV and information is available here.


  • Identify and analyze the fundamental concepts of the machine learning methods

  • Distinguish different machine learning methods for different types of data

  • Evaluate a suitable machine learning method for analyzing and solving business problems using software support

  • Analyze and compare the results of selected machine learning models

  • Choose the optimal machine learning model using appropriate tests


  • Graduate students in economics (or in social sciences, technical sciences or science related fields) who intend to round out their knowledge in the field and use it in the proces of preparing dissertations.
  • Holders of undergraduate degrees or students in the last year of ther studies in economics or the social sciences who wish to study the field of behavioural economics.
  • Researchers and professionals working in the field.


This FEBT Summer school program offers participants the possibility of going through evaluation process for the purspose of requesting official credit transfers (ECTS).

  • Evaluation process

Participants who wish to acquire 3 ECTS will be evaluated based on the regular and active course attendance and taking two mid-term exams and writing a final paper assignment (the project task is individual or group work which implies selection of the suitable machine learning method for a particular problem, analysis and comparison of the results and selection of the optimal model).


Participants not interested in credit transfer will instead receive the Certificate of Attendence, stating the course completed. These students will be neither evaluated nor graded.