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Seminar Artificial Intelligence

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

Title Slides / Code Material
Prerequisites
Please make sure to have running R and RStudio instances. Use this script to download and install required packages

Introduction
Exercise: Types of Machine Learning
Unsupervised Learning
Unsupervised Learning: k-means
Getting to know KNIME


Exercise: k-means





Unsupervised Learning: hierarchical clustering
Exercise: hierarchical clustering



Unsupervised Learning: DBScan
Unsupervised Learning: Wrapping up
Supervised Learning
Exercise: Gradient Boosting Machines
Supervised Learning: Overfitting
Exercise: Cross-Validation
Exercise: Decision Trees

Supervised Learning: Selecting the right model
Exercise: Precision and Recall in Multiclass Classification
Precision and Recall in Multiclass Classification
Exercise: Interpretability versus Explainability
From Neural Nets to Deep Learning
Exercise: Neural Networks





Large Language Models
Trustworthiness & Ethical Considerations
Exercise: Trustworthy AI
Exercise: Large Language Models & their application
Exercise: LLMs and their Limitations
Exercise: Setup your own!
Exercise: Chatbots and Knowledge Bases

Solutions

This section will show solutions to tasks once we discussed them in the course.

R Tutorials

In this section, you will find a list of tutorial videos and slides helping you to prepare the course.

Section Title
Prerequisites Getting Help
Prerequisites Find Help on the Internet
Prerequisites Thinking in Scripts
Basics Getting Started in RStudio
Basics About Variables
Basics About Vectors
Basics About Dataframes
Basics Modifying dataframes
Basics for-loops
Basics sapply-loops
Basics When to use sapply vs. when to use for
Basics Looping(s) through datasets

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.
  • Samuel R. Bowman. Eight things to know about large language models. CoRR, 2023. arXiv:2304.00612.
  • 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.
  • Kenneth Church, Annika Schoene, John E. Ortega, Raman Chandrasekar, and Valia Kordoni. Emerging trends: unfair, biased, addictive, dangerous, deadly, and insanely profitable. Natural Language Engineering, 29(2):483–508, 2023. doi:10.1017/S1351324922000481.
  • 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.
  • Plamen P. Angelov, Eduardo A. Soares, Richard Jiang, Nicholas I. Arnold, and Peter M. Atkinson. Explainable artificial intelligence: an analytical review. WIREs Data Mining and Knowledge Discovery, 11(5):e1424, 2021. doi:https://doi.org/10.1002/widm.1424.
  • 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.
  • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, and Rosaria Silipo. Guide to Intelligent Data Science: How to Intelligently Make Use of Real Data. Springer International Publishing, 2020. ISBN 9783030455743. https://www.datascienceguide.org/, doi:10.1007/978-3-030-45574-3.
  • Duri Long and Brian Magerko. What is ai literacy? competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20, 1–16. New York, NY, USA, 2020. Association for Computing Machinery. doi:10.1145/3313831.3376727.
  • Eirini Ntoutsi et al. Bias in data-driven artificial intelligence systems—an introductory survey. WIREs Data Mining and Knowledge Discovery, 10(3):e1356, 2020. doi:10.1002/widm.1356.
  • 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.

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