DS405B Practical Deep Learning from Visual Data
| Lecturer: | Aseem Behl, PhD |
| Course description: | DS405B |
| Language: | English |
| Recommended for this semester or higher: | 1 |
| ECTS-Credits: | 6 |
| Course can be taken as part of following programs/modules: | Economics and Finance Data Science in Business and Economics |
| Prerequisite for: | --- |
| Prerequisites: | Exam DS404 Data Science with Python successfully passed |
| Limited attendance: | 24 |
| Course Type: | Lecture |
| Date: | Weekly online live sessions: Tuesdays from 8:15am s.t. - 9:45am (Beginning of the first lecture April 20, 2021) |
| Registration: | Limited to 24 participants. Registration open for all (no first-come, first-served): Registration is open in Alma - end of registration time: April 11. If the number of applications (limited to 24 participants) 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. |
| Downloads: | ILIAS |
| Method of Assessment: | Written Exam or Presentation or Assignments or Term Paper |
| Content: | Deep learning has become widely successful in the automation of solutions to problems arising in computer vision, language processing, robotics, and many more application areas. 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 learning from visual data for applications in computer vision, however, several concepts learned in the module can be applied to other data modalities. |
| 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 image datasets to reliably train and debug modern deep learning techniques for computer vision 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: 1. Neural Networks and Deep Learning by Michael Nielsen 2. Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville 3. Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola |