Data Science and Artificial Intelligence

Advanced topics in artificial intelligence

Integrated course, 2.50 ECTS


Course content

Part 1: Advanced KI-powered applications
- Semantic text analysis and text synthesis, natural language processing
- Biometric analysis
- Generation of synthetic data sets
- Other advanced KI-powered applications
Part 2: Methods of Artificial Intelligence in Practice
- Field of application as well as advantages and disadvantages of different KI methods
- Hybrid approaches (fuzzy neural approaches etc.)
- Selection of suitable AI methods for specific problems
- Typical mistakes and problems as well as their avoidance or reduction
- New approaches in artificial intelligence and computational intelligence

Learning outcomes

Students understand and are able to perform advanced KI-based analyses in different application domains. In addition, they know the advantages and disadvantages as well as the fields of application of different methods from the field of artificial intelligence and are able to avoid or reduce typically occuring errors and problems. Furthermore, students are familiar with modern approaches to artificial intelligence.

Recommended or required reading and other learning resources / tools

Recommended literature or books:
- Aggarwal, C. C. (2018). Neural Networks and Deep Learning: A Textbook. Springer, 1st edition.
- Bishop, C. M. (2011). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 1st edition (reprint 2011).
- Deru, M., Ndiaye, A. (2019). Deep Learning mit TensorFlow, Keras und TensorFlow.js: Einstieg, Konzepte und KI-Projekte mit Python, JavaScript und HTML5. Rheinwerk Computing, 1. Auflage.
- Engelbrecht, A. P. (2007).Computational Intelligence: An Introduction. Wiley, 2nd edition.
- Ertel, W. (2016). Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung (Computational Intelligence). Springer Vieweg, 4. Auflage.
- Kruse, R., Borgelt, C. et al (2015). Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze. Springer Vieweg, 2. Auflage.
- Pfister, B., Kaufmann, T. (2017). Sprachverarbeitung: Grundlagen und Methoden der Sprachsynthese und Spracherkennung. Springer Vieweg, 2. Auflage.
Recommended journals or selected articles:
Relevant journals and articles will be announced in the courses.

Typical software for this module:
Python/Spyder/PyCharm/Scikit-learn/TensorFlow/Keras/PyTorch, Matlab/Octave/Scilab etc.

Mode of delivery

1,25 ECTS Lecture, 1,25 ECTS Exercise

Prerequisites and co-requisites

Module 7,8,9 and 10

Assessment methods and criteria

Lecture: final exam; Exercise: examination character