30422 - TECHNOLOGICAL INNOVATION SEMINARS II
Department of Decision Sciences
Course taught in English
Go to class group/s: 25
Course Director:
OMIROS PAPASPILIOPOULOS
OMIROS PAPASPILIOPOULOS
Suggested background knowledge
Machine Learning
Computer Programing
Mission & Content Summary
MISSION
This seminar introduces agentic AI systems for enterprise automation, emphasizing autonomous agent architectures, compliance, and responsible governance frameworks. Students examine academic literature and prototype agent use-cases with no-code tools.
CONTENT SUMMARY
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Understand agentic AI conceptual foundations and taxonomy
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Apply leading responsible AI governance models in regulated contexts
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Prototype an agentic AI workflow using a no-code framework
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
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Understand agentic AI conceptual foundations and taxonomy
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Prototype an agentic AI workflow using a no-code framework
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
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Apply leading responsible AI governance models in regulated contexts
Teaching methods
- Lectures
- Practical Exercises
- Collaborative Works / Assignments
- Interaction/Gamification
DETAILS
Each method presented in the face-to-face lectures is directly motivated and illustrated on a number of relevant case studies from political sciences, neurosciences and criminology. These case studies showcase the potentials of Data Science, Machine Learning and AI in the specific field of Network Science.
Assessment methods
| Continuous assessment | Partial exams | General exam | |
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ATTENDING STUDENTS
Full credit is assigned automatically to all students who meet the active class participation condition (3 or 4 classes with registered attandance out the 4 total classes). These students do not need to take the oral exam.
NOT ATTENDING STUDENTS
For students who do not meet the active class participation condition, the full credit is assigned after a successful oral individual exam on the topics presented during the course.
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Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
The course is entirely based on slides and research articles. All slides and research articles are made available to both attending and non-attending students.