| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st Master of Business and Information Systems Engineering | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| The holder of the degree applies acquired knowledge independently. (Self-direction and entrepreneurial spirit) | - EC
| The holder of the degree shows autonomy in implementing scientific research methods. (Research skills) | - EC
| The holder of the degree models, designs and evaluates solutions for business and IT problems to support decision-making at different levels in a complex context. (Problem-solving capacity) | - EC
| The holder of the degree uses data science and IT to design decision support systems that provide useful insights with which the quality of decisions can be improved. (Programme-specific competencies) |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The following data mining/machine learning methods will be covered
- classification and estimation
- network analysis
- clustering
- association analysis
Applications
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Lecture ✔
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Small group session ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 25 % |
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| Transfer of partial marks within the academic year | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | written closed-book exam (100%) |
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| Compulsory textbooks (bookshop) |
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| Recommended reading |
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Data Mining: Practical Machine Learning Tools and Techniques,Ian H. Witten; Eibe Frank; Mark A. Hall,3,Morgan Kaufmann,9780128042915,Beschikbaar als e-book: https://www-sciencedirect-com.bib-proxy.uhasselt.be/book/9780123748560/data-mining-practical-machine-learning-tools-and-techniques |
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| Exchange Programme Business Economics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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The following data mining/machine learning methods will be covered
- classification and estimation
- network analysis
- clustering
- association analysis
Applications
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
|
|
|
|
Paper ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 Credits 6,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 25 % |
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|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | written closed-book exam (100%) |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
|
|
| Recommended reading |
| |
Data Mining: Practical Machine Learning Tools and Techniques,Ian H. Witten; Eibe Frank; Mark A. Hall,3,Morgan Kaufmann,9780128042915,Beschikbaar als e-book: https://www-sciencedirect-com.bib-proxy.uhasselt.be/book/9780123748560/data-mining-practical-machine-learning-tools-and-techniques |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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