Hello!
Thanks for your interest in my work and the topic Artifical Intelligence in Information Systems. In this repository you find my teaching and course material for the university course AI Algorithms and Applications with Python. I teach this course since 2019 at Darmstadt University of Technology. The course gives an introductory survey of AI techniques in Python for master students with no or little experience to this subject. Prerequisites are basic programming skills, algorithms, and calculus. Some exposure to linear algebra and probability is a plus and will definitly help you.
The course itself is organised in the following chapters:
| ID | CHAPTER |
|---|---|
| 1 | Introduction into Artificial Intelligence |
| 2 | Search, Problem Solving, and Planning |
| 3 | AI-Programming with Python |
| 4 | Data and Feature Engineering with Python |
| 5 | Knowledge Reasoning and Representation |
| 6 | Machine Learning |
| 7 | Artificial Neural Networks and Deep Learning |
| 8 | Probabilistic Reasoning and Modelling |
| 9 | Natural Language Processing |
| 10 | Computer Vision |
| 11 | Building Productive AI-based Systems |
For each chapter, you can find the accompanying material in the different folder of this repository. The best practice is that you clone this git repository (for more information see the slides of the first lecture)! Also take a look at the lecture syllabus for further course information.
The related course recordings are online available in my youtube channel (see here). The full lecture is about two semesters and is equal to 27 traditional 90-minutes units (without exercises).
Literature recommendations for this course are:
- Rusell, S., & Norvig, P. Artificial Intelligence: A Modern Approach.
- Géron, A.: Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
- Jung, D. The Modern Business Data Analyst: A Case Study Introduction into Business Data Analytics with CRISP-DM and R.
- Reis, J., & Housley, M. Fundamentals of Data Engineering: Plan and Build Robust Data Systems.
- Castillo, E., Gutierrez, J. M., & Hadi, A. S. Expert Systems and Probabilistic Network Models. Springer Science & Business Media.
I try continuously to improve and update the material - so if you have any complaints, wishes, ideas or questions please feel free to contact me. If you are interested in general at data science and information systems you can also take a look at my other lectures (see here).
If you have further questions, you can contact me by mail (see lecture slides) or add a comment in the git repository.
Have a nice day!
Dominik