Event
Artificial Intelligence in Service Systems - Applications in Computer Vision [SS212595501]
Lecturers
Organisation
- Karlsruhe Service Research Institute
Part of
- Brick Artificial Intelligence in Service Systems - Applications in Computer Vision | Industrial Engineering and Management (M.Sc.)
- Brick Artificial Intelligence in Service Systems - Applications in Computer Vision | Economics Engineering (M.Sc.)
- Brick Artificial Intelligence in Service Systems - Applications in Computer Vision | Information Systems (M.Sc.)
- Brick Artificial Intelligence in Service Systems - Applications in Computer Vision | Information Engineering and Management (M.Sc.)
Literature
- Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
- Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach.
- Goldstein, E. B. (2009). Sensation and perception. 8th. Belmont: Wadsworth, Cengage Learning, 496(3).
- Gonzalez, Rafael C., Woods, Richard E. (2018). Digital Image Processing. 4th Pearson India
- Szeliski, R. (2010). Computer vision: algorithms and applications. Springer Science & Business Media.
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 779-788).
- Sermanet, P., Chintala, S., & LeCun, Y. (2012, November). Convolutional neural networks applied to house numbers digit classification. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)(pp. 3288-3291). IEEE.
- Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems(pp. 91-99).
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 580-587).
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems(pp. 1097-1105).
Appointments
- 16.04.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 23.04.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 30.04.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 07.05.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 14.05.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 21.05.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 04.06.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 11.06.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 18.06.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 25.06.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 02.07.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 09.07.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 16.07.2021 08:00 - 13:30 - Room: 05.20 1C-01
- 23.07.2021 08:00 - 13:30 - Room: 05.20 1C-01
Note
Learning objectives
This course teaches students how to apply machine learning concepts to develop predictive models that form the basis of many innovative service offerings and business models today. Using a selected use case each term, students learn the foundations of selected algorithms and development frameworks and apply them to build a functioning prototype of an analytics-based service. Students will become proficient in writing code in Python to implement a data science use case over the course period.
Description
Data-driven services have become a key differentiator for many companies. Their development is based on the increasing availability of structured and unstructured data and their analysis through methods from data science and machine learning. Examples comprise highly innovative service offerings based on technologies such as natural language processing, computer vision or reinforcement learning.
Using a selected use case, this lecture will teach students how to develop analytics-based services in an applied setting. We teach the theoretical foundations of selected machine learning algorithms (e.g., convolutional neural networks) and development concepts (e.g., developing modeling, training, inference pipelines) and teach how to apply these concepts to build a functioning prototype of an analytics-based service (e.g., inference running on a device). During the course, students will work in small groups to apply the learned concepts in the programming language Python using packages such as Keras, Tensorflow or Scikit-Learn.
Recommendations
The course is aimed at students in the Master's program with basic knowledge in statistics and applied programming in Python. Knowledge from the lecture Artificial Intelligence in Service Systems may be beneficial.
Additional information
Due to the practical group sessions in the course, the number of participants is limited. The offiicial application period in the WiWi portal is over. However, there is a limited number of remaining spaces. In case you are motivated to participate and have previous experience in the fields of Python Programming and Machine Learning please send a mail to jannis.walk∂kit.edu until Friday, 9th of April 2021.
Your mail has to contain:
- A short letter of motivation, ideally (but not necessarily) with reference to previous experience in programming and data science (maximum one page)
- Transcript of records (for Bachelor and Master if available)