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AI, artificial intelligence, smart devices, machine learning… These terms are heard more and more frequently. AI is
penetrating many areas of human activity, from drug development to partner selection.
The course “Elements of AI 2.0” is designed for complete beginners in the field of AI and introduces the topic without the
need for programming.
The course consists of an online module, several in-person sessions, and a final assignment. More at www.aivk.cz.
Last update: Holan Tomáš, RNDr., Ph.D. (21.05.2025)
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Course Objective: This is a foundational course for complete beginners, especially intended for students outside the Faculty of Mathematics and Physics (MFF). The course consists of three parts: the online course (Elements of AI), participation in an in-person lecture, and a final workshop.
After successfully completing the online course, students will understand: • What autonomy and adaptability mean in the context of AI • How the Turing test works • How to formulate a real-world problem in a form suitable for search • What a neural network is • What technical methods underlie neural networks • How difficult it is to predict the future • What the main societal impacts of AI are, including algorithmic bias and AI-generated content
Through a selected lecture, students will deepen their understanding of a specific area of AI (e.g., AI in healthcare, law, transportation, or drone usage). The final workshop serves to consolidate the knowledge gained and to present the final assignment. During the workshop, students will share their topics with peers and thus gain insight into other areas of human activity.
Last update: Holan Tomáš, RNDr., Ph.D. (21.05.2025)
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NOTE THAT THIS COURSE IS CURRENTLY ONLY OFFERED IN CZECH LANGUAGE. You can do the online course in English, but you cannot get credits for that.
Course Completion Requirements: � • Successfully complete the online course “Elements of AI” in Czech or English at: https://course.elementsofai.com� • Submit a link to the certificate� • Submit the final assignment via a form • Attend at least one lecture from our or a personal selection. A list of events will be published at www.aivk.cz� • Attend the final workshop during the exam period (following the MFF UK academic calendar)� � The course is graded as pass/fail (without numeric grading).� The course may only be taken once within one faculty (not even under a different code, such as TVOL0007 or NAIL 130).� � About the Online Course� The course was created at the University of Helsinki in collaboration with MinnaLearn and is distributed in the Czech Republic by prg.ai. Completion is recommended at least 14 days before the desired certificate deadline (peer-review may take some time).� Assessment is based on quizzes, numerical exercises, and written responses, which are evaluated automatically, by peers, or by instructors.� � Requirements for successful completion:� • Completion of at least 90% of the exercises� • At least 50% correct answers� Taking the course in English is preferred, but Czech is acceptable if necessary. Last update: Rosa Rudolf, Mgr., Ph.D. (21.01.2026)
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Literature:
For the online course: • Direct link to the course: https://course.elementsofai.com • More information: https://www.elementsofai.com/ | https://www.elementsofai.cz
On AI in general: • Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (4th Edition). Pearson, 2020. http://aima.cs.berkeley.edu • Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. https://www.deeplearningbook.org/
AI in various fields: • Gundersen, T., & Bærøe, K. (2022). The Future Ethics of Artificial Intelligence in Medicine: Making Sense of Collaborative Models. Science and Engineering Ethics, 28(2), 1-16. • Schapals, A. K., & Porlezza, C. (2020). Assistance or resistance? Evaluating the intersection of automated journalism and journalistic role conceptions. Media and Communication, 8(3), 16-26. • Liao, S. M. (Ed.). (2020). Ethics of artificial intelligence. Oxford University Press Last update: Holan Tomáš, RNDr., Ph.D. (21.05.2025)
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Teaching Methods: The course consists of an online part(Elements of AI), attending a lecture or workshop, a final assignment, and presentation at the final workshop. Students choose their own AI-related topic with relevance to their field of study, develop it, and share it with peers. The goal is to gain a basic overview, deepen understanding in one specific topic, and share insights with fellow students. Last update: Holan Tomáš, RNDr., Ph.D. (21.05.2025)
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Syllabus:
1) Online Part: The course contains 6 chapters: • What is AI? • Solving problems with AI • AI in the real world • Machine learning • Neural networks • Implications Each chapter contains text and interactive elements. Total time commitment is 20-30 hours.
2) In-Person Part: Attendance at a selected lecture, independent preparation, and presentation during the final workshop (3-4 hours). Last update: Holan Tomáš, RNDr., Ph.D. (21.05.2025)
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