Courses Bachelor Display 2025-2026
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| Course title | Deep Learning for (Un)structured Data | |||||||||||||||||||||||||||||||||||||||
| Course code | EBC2200 | |||||||||||||||||||||||||||||||||||||||
| ECTS credits | 6,5 | |||||||||||||||||||||||||||||||||||||||
| Assessment | None | |||||||||||||||||||||||||||||||||||||||
| Period |
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| Level | Advanced | |||||||||||||||||||||||||||||||||||||||
| Coordinator |
Rui Jorge De Almeida e Santos Nogueira For more information: rj.almeida@maastrichtuniversity.nl |
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| Language of instruction | English | |||||||||||||||||||||||||||||||||||||||
| Goals |
Deep Learning is a fundamental block in AI. This course is a deep dive into the details of deep learning architectures, where you will understand how to build neural networks, with a focus on learning end-to-end models for unstructured data.
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| Description |
This course will cover several deep learning algorithms. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, GenAI, Reinforcement Learning amongst other subjects. We will discuss theoretical properties of the methods, their practical implementation using a suitable programming language (e.g. Python). This course relates to several application areas where business problems are supported using systematic data analysis.
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| Literature |
• Goodfellow, I., Bengio, Y. Courville, A. (2016). Deep Learning. MIT
Press. ISBN: 978-0-262-035613. Freely available at: http://www.deeplearningbook.org. • Sutton, R. S. (2018). Reinforcement learning: An introduction. A Bradford Book. • Stevens, E., Antiga, L., & Viehmann, T. (2020). Deep learning with PyTorch. Manning Publications. ISBN: 9781617295263 • Shukla, N., & Fricklas, K. (2018). Machine learning with TensorFlow. |
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| Prerequisites |
Students need to have solid background in probability theory, mathematical statistics, and programming in Python.
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| Keywords |
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| Teaching methods (indicative; course manual is definitive) | PBL / Lecture | |||||||||||||||||||||||||||||||||||||||
| Assessment methods (indicative; course manual is definitive) | Final Paper / Participation / Assignment | |||||||||||||||||||||||||||||||||||||||
| 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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