Courses Master Display 2022-2023
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| Course title | Machine Learning for Smart Services | |||||||||||||||||||||||||||||||||||||||
| Course code | EBC4255 | |||||||||||||||||||||||||||||||||||||||
| ECTS credits | 5,0 | |||||||||||||||||||||||||||||||||||||||
| Assessment | Whole/Half Grades | |||||||||||||||||||||||||||||||||||||||
| Period |
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| Level | Advanced | |||||||||||||||||||||||||||||||||||||||
| Coordinator |
Özge Tüncel For more information: o.tuncel@maastrichtuniversity.nl |
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| Language of instruction | English | |||||||||||||||||||||||||||||||||||||||
| Goals |
After completing this course you:
* Know the relationship between machine learning, artificial intelligence and smart services. * Will be able to design and implement intelligent systems. * Will be able to reflect on and evaluate intelligent systems. |
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| Description |
PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.
Smart services or (more generally) intelligent systems rely on data to automate processes or assist human decisions. There are numerous domains in which they are applied such as predictive maintenance and recommender systems. They all have a set of components in common: input, output and a controller, but they vary on different aspects: such as the level of automation, ranging from suggesting actions to a user to automated, autonomous actions. After following Machine Learning for Smart Services you will understand the concept of intelligent systems, such as what constitutes them and when they are useful to implement. Building on the knowledge you gained in the course Business Analytics you will learn how to implement and evaluate them. |
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| Literature |
* Hulten, Geoff (2018). Building Intelligent Systems: A Guide to Machine Learning Engineering. New York, NY: Apress [ISBN 978-1-4842-3431-0]
* Additional Papers |
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| Prerequisites |
* Experience with programming in R
* Basic understanding of predictive modeling and model evaluation |
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| Keywords |
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| Teaching methods (indicative; course manual is definitive) | PBL / Lecture / Assignment / Papers / Research | |||||||||||||||||||||||||||||||||||||||
| Assessment methods (indicative; course manual is definitive) | Final Paper / Written Exam / Presentation | |||||||||||||||||||||||||||||||||||||||
| Evaluation in previous academic year | For the complete evaluation of this course please click "here" | |||||||||||||||||||||||||||||||||||||||
| This course belongs to the following programmes / specialisations |
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