JBP040 :Introduction to Machine Learning


Voertaal Engels
Werkvorm: Lectures and instructions/labs (Collegerooster)
Tentamenvorm: (Tentamenrooster)
Studielast:6 ECTS credits
Blackboard informatieniet beschikbaar in Blackboard


D.A. Tamburri (Coördinator)

N.R. Claus MSc

Doel van de cursus (alleen in het Engels beschikbaar)

The "introduction to data mining" course then will then cover basic topics including decision trees and model evaluation, and, introduce association analysis.

Data mining studies how to induce predictive models and to gain useful (ationable) insights from the data with help of some computational tools.

This introductory course covers the following topics:

  • Data mining end-to-end process, starting from translation of the business ¿problem to data mining task(s) and
  • data preparation for modeling and ending with evaluation of the data mining outcomes and reporting.
  • Data mining techniques for classification (Bayesian methods, instance-based methods, decision trees, and ensembles), clustering (kMeans, DBSCAN, AHC), frequent itemset and association rule mining (Apriori), feature subset selection and data transformation.
  • Evaluation of data mining output, model performance optimization and avoiding overfitting. Comparing performance of different techniques.

Inhoud van de cursus (alleen in het Engels beschikbaar)

During this course the students are expected to learn the foundations of data mining and gain hands-on experience of applying data mining in practice.
After taking the course, each student:
  • Understands and can explain the basic principles and techniques of data mining. 

  • Is aware of various application areas of data mining. 

  • Understands and can explain when data mining might be useful.
  • Is capable of translating business problems to data mining tasks and choosing appropriate data mining techniques. 

  • Has the skills for designing, developing and evaluating data mining solutions using exciting data mining software like This includes: 

  • transforming raw data like a collection of texts or a database of transactions to a representation that can be understood by data mining techniques;

  • choosing appropriate techniques for data preprocessing, basic modeling and evaluation, optimization of parameters for defined KPIs, e.g. cost-sensitive classification, for the algorithms available in Weka;

  • making valid conclusions about the performance of the models and their utility for addressing the identified business problem.

Bijzonderheden (alleen in het Engels beschikbaar)

The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program.

Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS).

Verplichte literatuur

  1. A blend of research articles, class notes, and material from reference books will be used in this course..

Mogelijk interessant voor

  • Pre-master Data Science and Entrepreneurship ( 2016, 2017 )