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Course module: JBP040-B-6
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
Contact personD.A. Tamburri
Lecturer(s)
Contact person for the course
D.A. Tamburri
Other course modules lecturer
lecturer
D.A. Tamburri
Other course modules lecturer
Coordinator course
D.A. Tamburri
Other course modules lecturer
Starting block
SM 1/  SM 2
Course mode
Full-time
Remarks-
Registration openfrom 14/08/2018 09:00 up to and including 31/07/2019
Aims
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.
Content
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)
Required materials
Manual
https://www.rstudio.com/
Reader
-
Title:Machine Learning
Author:T. Mitchell
Publisher:McGraw Hill
Edition:3
Manual
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Title:The Weka Workbench
Author:Eibe Frank, Mark A. Hall, and Ian H. Witten
Publisher:MK Assoc.
Edition:3
Recommended materials
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