Kies de Nederlandse taal
Course module: JBP040-B-6
Introduction to Machine Learning
Course info
Course moduleJBP040-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Management; TiSEM: Management;
Is part of
PM Academic premaster
PM Data Science & Entrepreneurship
Convenant TISEM
D.A. Tamburri, PhD
Other course modules lecturer
Academic year2018
Starting block
SM 1/  SM 2
Course mode
RemarksThis information is not up to date. Check the Course Catalog 2019 or select the course via “Register”.
Registration openfrom 14/08/2018 09:00 up to and including 31/07/2019
The "introduction to Machine Learning" course will cover basic topics in Data Mining and Machine leaarning, leading from the design of a proper data-scientific study campaign which starts from data mining and preparation and proceeds to experimentation with ML algorithms. Known frameworks for Data Mining (i.e., CRISP-DM) will be considered and experimented upon practically. Furthermore, the student will learn the basics of research design and hypothesis formulation/testing. Subsequently, the student will get to grips with most commonly used techniques of machine-learning including decision-trees, instance-based learning, as well as artificial neural networks. Finally, the student will learn the basics of model evaluation, model generalization as well as the bias-variance tradeoff.
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 (e.g., feature subset selection and data transformation) for modeling and ending with evaluation of the data mining outcomes and reporting.
  • Machine-Learning techniques for classification (Instance-based methods, decision trees, ANNs and ensembles).
  • Evaluation of Machine-Learning output, model performance optimization through boosting as well as avoiding overfitting while trading-off over bias and variance.
  • Comparing performance of different techniques.
The student will experiment practically with the studied techniques both in a project and in-course practical sections.
During this course the students are expected to learn the foundations of Machine-Learning as well as data mining for machine-learning purposes; the student will gain hands-on experience of applying both in practice.

After taking the course, each student:
  • Understands and can explain the basic principles and techniques of machine learning and data mining.
  • Is aware of the involved application areas
  • Understands and can explain when data mining and machine-learning are useful in a value-generating sense
  • Is capable of translating business problems to data mining as well as machine-learning tasks and choosing appropriate data mining techniques.
  • Has the skills for designing, developing and evaluating machine-learning solutions using exciting specific software packages
  • Transforming raw data like a collection of texts or a database of transactions to a representation that can be understood by the known 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, R, or other software;
  • making valid conclusions about the performance of the models and their utility for addressing the identified business problem.
Type of instructions
Lectures and instructions/labs

Compulsory Reading
  1. A blend of research articles, class notes, and material from reference books will be used in this course..
Timetable information
JBP040-B-6|Introduction to Machine Learning (Fall)
Written test opportunities
Written test opportunities (HIST)
Schriftelijk / WrittenEXAM_01SM 1113-12-2018
Schriftelijk / WrittenEXAM_01SM 1217-01-2019
Required materials
Title:Machine Learning
Author:T. Mitchell
Publisher:McGraw Hill
Title:The Weka Workbench
Author:Eibe Frank, Mark A. Hall, and Ian H. Witten
Publisher:MK Assoc.
Recommended materials

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