This is the main course website for the seminar "Artificial Intelligence". This is a living document and new content will be added here. The seminar is part of a series that started with "Software Technologies and Applied Programming" and continues with "Intelligent Data Processes".
Course Description¶
The application areas of artificial intelligence, machine learning and the application-specific aspects of AI processes and projects are dealt with in depth. The efficient application of the learned methods for the use of AI in terms of an AI-based hybrid value creation are designed.
Slides¶
R Tutorials¶
In this section, you will find a list of tutorial videos and slides helping you to prepare the course.
Prerequisites¶
| Title | Video | Material | |
|---|---|---|---|
| Getting Help | |||
| Find Help on the Internet | |||
| Thinking in Scripts |
Basics¶
The full list of tutorial videos introducing R is available online. Feel free to peek into topics that interest you. You can also check out the course website of my R tutorial which puts the tutorial videos into better order.
Legend¶
| Session was a lecture | |
| Session was a practical | |
| Find slides here | |
| Find code material here | |
| Find external material here | |
| This session has a self assessment quiz attached |
References¶
- Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, and Simone Stumpf. Explainable artificial intelligence (xai) 2.0: a manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106:102301, June 2024. http://dx.doi.org/10.1016/j.inffus.2024.102301, doi:10.1016/j.inffus.2024.102301.
- Dan Jurafsky and James H. Martin. Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition. Pearson Prentice Hall, 2024.
- Gary Marcus and Reid Southen. Generative ai has a visual plagiarism problem. 2024. [Online; accessed 2-June-2024]. https://spectrum.ieee.org/midjourney-copyright.
- Henner Gimpel, Kristina Hall, Stefan Decker, Torsten Eymann, Luis Lämmermann, Alexander Mädche, Maximilian Röglinger, Caroline Ruiner, Manfred Schoch, Mareike Schoop, Nils Urbach, and Steffen Vandirk. Unlocking the power of generative ai models and systems such as gpt-4 and chatgpt for higher education : a guide for students and lecturers. March 2023. Whitepaper. https://www.fim-rc.de/Paperbibliothek/Veroeffentlicht/1594/wi-1594.pdf.
- Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. Sparks of artificial general intelligence: early experiments with gpt-4. 2023. arXiv:2303.12712.
- Maximilian Pichler and Florian Hartig. Machine Learning and Deep Learning with R. Online, 2023. https://theoreticalecology.github.io/machinelearning/.
- Stuart Russell and Peter Norvig. Artificial intelligence: A modern approach, global edition. Pearson Education, London, England, 4 edition, December 2021.
- Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of stochastic parrots: can language models be too big? 🦜. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT '21, 610–623. New York, NY, USA, 2021. Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922, doi:10.1145/3442188.3445922.
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer US, 2021. ISBN 9781071614181. http://dx.doi.org/10.1007/978-1-0716-1418-1, doi:10.1007/978-1-0716-1418-1.
- Wolfgang Ertel. Grundkurs Künstliche Intelligenz. Springer Fachmedien Wiesbaden, 2016. ISBN 9783658135492. http://dx.doi.org/10.1007/978-3-658-13549-2, doi:10.1007/978-3-658-13549-2.
- Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. Mining of Massive Datasets. Cambridge University Press, November 2014. ISBN 9781139924801. http://dx.doi.org/10.1017/CBO9781139924801, doi:10.1017/cbo9781139924801.