SylabUZ
| Course name | Artificial Intelligence in Decision-Making |
| Course ID | 04.9-WZ-P-AIiDM- 23 |
| Faculty | Faculty of Economics and Management |
| Field of study | WEiZ - oferta ERASMUS |
| Education profile | - |
| Level of studies | Erasmus programme |
| Beginning semester | winter term 2023/2024 |
| Semester | 1 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
|
| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Laboratory | 30 | 2 | - | - | Credit with grade |
Student is familiar with some methods and tools for decision making in relation to AI.
None
This course is an introduction to some central issues in decision theory and their relationship to artificial intelligence (AI).
Automated systems have a wide range of applications, ranging from self-driving cars to chess computers. An autonomous vehicle is equipped with built-in processors and sensors that can detect the environment, perform sensor fusion for decision making, and have continuous control and steering.
With ChatGPT's advanced language processing capabilities, it can be used to automate these processes and provide more accurate and efficient decision-making. it would allow AI systems to better understand the context of a situation and make more informed decisions . For example, ChatGPT can be used to generate personalized product recommendations for customers based on their browsing history and purchase behavior
This course introduces traditional decision-theoretic tools and models and discusses the bearing of these to core issues in the philosophy of AI.
The teaching consists of lectures and project method.
| Outcome description | Outcome symbols | Methods of verification | The class form |
The examination consists of active participation in seminars and written assignments.
Student final grade will be a combination of: written exam, individual coursework, class participation.
1. Peterson M., An introduction to decision theory, 2. ed. : Cambridge : Cambridge University Press : 2017, ISBN: 9781107151598.
2. Russell, S., Artificial Intelligence: A Modern Approach, 4th edition, 2021.
3. Wang, F. et al., Chat with ChatGPT on Industry 5.0: Learning and Decision-Making for Intelligent Industries, IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL. 10, NO. 4, 2023
4. Brynjolfsson E, McAfee A., The Business of Artificial Intelligence, Harvard Business
Review, 2017.
5. Sharda, R., Delen, D., Turban, E., Business intelligence and analytics: systems for decision support, Pearson Education Limited, 2014. ISBN: 9781292009261.
1. Agrawal, A. K., Gans, J. S., Goldfarb A., What to Expect From Artificial Intelligence, MIT Sloan Management Review, 2017, ISBN: 53863MIT58311
2. Lantz, B., Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition. Packt Publishing, 2019.
3. Poole, D., Mackworth, A. Artificial Intelligence - Foundations of Computational Agents. New York: Cambridge University Press. 2nd Edition, 2017.
4. Cunneen, M., Mullins, M., & Murphy, F. Autonomous Vehicles and Embedded Artifcial Intelligence: The Challenges of Framing Machine Driving Decisions. Applied Artifcial Intelligence, 33(8), 706–731. https://doi.org/10.1080/08839514.2019.1600301
Lecturer: w.wasilewski@wez.uz.zgora.pl
Modified by mgr inż. Wiesław Wasilewski (last modification: 01-06-2023 09:52)
