DS407 Practical Deep Learning for Language Processing
| Lecturer: | Dr. Aseem Behl |
| Course code: | DS407 |
| Language: | English |
| Recommended for this semester or higher: | 1 |
| ECTS-credits: | 6 |
| Course can be taken as part of following programs/modules: | See alma |
| Prerequisites: | Prior experience with programming in Python ideal but not necessary, e.g., through an introductory Python course like DS400 Data Science Project Management. |
| Limited attendance: | 28 |
| Course Type: | Lecture |
| Date: | Tuesdays, 10 a.m. - 12 a.m. First lecture on October 14, 2025 |
| Registration: | By October 19 via alma
If the number of applications exceeds the number of places available, we will randomly select from all applications. Preferred access for students from the M.Sc. Data Science in Business and Economics program. |
| Method of Assessment: | Assignments |
| Content: | Deep learning has become widely successful in tackling fundamental tasks arising in computer vision, language processing and robotics. Many of these fundamental tasks are relevant to a much broader array of applications in business and scientific domains. This module starts with a broad view of machine learning and neural networks, and it subsequently covers the theory of neural networks in the context of practical examples and implementation of deep learning methods with the help of prominent frameworks in Python. The focus will be on applications deriving business intelligence from text data, however, several concepts learned in the module can be applied to other data modalities like tabular or visual data. |
| Objectives: | After this module, students can develop an understanding of how neural network models work and how to implement neural network architectures in Python with the help of deep learning frameworks. They can exploit text datasets to reliably train and debug modern deep learning techniques for natural language processing tasks. They can appreciate the effectiveness of deep learning as a tool in their machine learning toolbox. |
| Literature: | There is no required textbook for this module. Some lectures may recommend readings from the following books:
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| Downloads: | --- |
| ILIAS: | TBD |