Knowledge discovery (1726) |
| Credits: 6,0 | | Study load hours: 162 | Period: semester 1 (6sp)  |
| Language of instruction: English | | Exam contract: not possible |
|
|
Advising sequentiality bound on the level of programme components
|
| |
| |
| |
Following programme components are advised to also be included in your study programme up till now.
|
| |
|
Business statistics (1738)
|
6.0 stptn | |
| |
|
Advanced Mathematics 1 (1536)
|
6.0 stptn | |
| |
|
Advanced mathematics 2 (4034)
|
3.0 stptn | |
| |
|
|
|
|
|
Knowledge Discovery (6 ECTS) is designed to build foundational skills in traditional machine learning and data mining for business applications. The course progresses systematically from core concepts to practical implementation, covering supervised learning (e.g., kNN, Decision Trees, Logistic Regression, Ensemble Methods), unsupervised techniques (e.g,. Clustering, Association Rule Mining), and advanced topics (e.g., SVM, Feature Engineering). Students gain essential skills in data preprocessing (using pandas/Colab), model evaluation (e.g., accuracy, precision, recall), and hyperparameter tuning (including cross-validation and automated methods)—all applied directly to real-world business challenges such as customer segmentation and churn prediction. Hands-on labs and a capstone project enable application to real datasets using Python.
|
|
| Compulsory textbooks (bookshop) |
| |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Aurélien Géron, 2022, 3rd edition, O'Reilly Media. ISBN 9781098122461 |
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
|
|
|
|
Exercises ✔
|
|
|
|
Semester 1 (6,00sp)
| Evaluation method | |
|
| Written evaluation during teaching period | 10 % |
|
| Transfer of partial marks within the academic year | Yes, no resit exam |
|
|
|
|
|
|
| Oral evaluation during teaching period | 10 % |
|
| Transfer of partial marks within the academic year | Yes, no resit exam |
|
|
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The score of the project presentation (10%) and submitted prediction
model (10%) is transferred. Students
must retake the written closed-book exam (80%). |
|
|
|
|
Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Business and Information Systems Engineering
|
- EC
| EC 14: 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
| EC 16: 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) |
|
|
|
Master of Business Engineering
|
- EC
| EC 14: The holder of the degree models, designs and evaluates solutions for financial and technical business problems to support decision-making at different levels in a complex context. (Problem-solving capacity) | - EC
| EC 16: The holder of the degree uses IT applications and basic programming skills to translate financial and technical business data into business-relevant information. (Programme-specific competencies) |
|
|
|
|
| Included in these programmes | Tolerance3 |
|
|
Y
|
|
Exchange Programme Business Economics
|
Y
|
|
|
1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
|
|
|