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Lecturer(s)
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Bureš Pavel, JUDr. Ph.D.
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Bělohlávek Radim, prof. RNDr. Ph.D., DSc.
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Course content
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1. Introduction to artificial intelligence. History of artificial intelligence. Philosophical aspects of artificial intelligence. 2.Selected areas and methods of artificial intelligence with emphasis on their nature and possibilities. Logical methods of artificial intelligence (classical and non-classical logic, automatic reasoning). Uncertainty handling (probabilistic models, fuzzy logic). Knowledge representation (logical models, concept representation, network models). Classification, clustering and decision making. Expert systems (rule-based and other systems). Neural networks and connectionism. Natural language processing and language models. Image processing and computer vision. Biologically inspired models. 3. Risks. Basic ethical and legal issues. The future of artificial intelligence.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training)
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Learning outcomes
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Information Technology for Law and Legal Science 3, which is taught in English language, is the third part of a three-semester course. It provides an introduction to artificial intelligence. The course focuses on its development, principles, capabilities and related societal aspects. It covers selected areas and methods with the aim of providing social science majors with an informed insight into the field of artificial intelligence. The course is divided thematically into the following sections: 1. Introduction to Artificial Intelligence. 1. Philosophical aspects of artificial intelligence. 2.Selected areas and methods of artificial intelligence with emphasis on their nature and possibilities. Logical methods of artificial intelligence (classical and non-classical logic, automatic reasoning). Uncertainty handling (probabilistic models, fuzzy logic). Knowledge representation (logical models, concept representation, network models). Classification, clustering and decision making. Expert systems (rule-based and other systems). Neural networks and connectionism. Natural language processing and language models. Image processing and computer vision. Biologically inspired models. 3. Risks. Basic ethical and legal issues. The future of artificial intelligence.
The course will provide students with an introductory knowledge of artificial intelligence
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Prerequisites
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Completion of this course is contingent upon completion of Information Technology for Law and Legal Science 1 and 2
MEP/TIT1 and MEP/TIT2
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Assessment methods and criteria
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Student performance
A full-time student is required to attend 80% of the course lectures. A combined form student is required to attend at least 30% of the lectures and one tutorial with the lecturer.
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Recommended literature
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BARFIELD, W., U., PAGALLO, U. (2020). Advanced Introduction to Law and Artificial Intelligence. Edward Elgar Publishing.
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BOSTROM, N. (2017). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
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CUSTERS, B., E., FOSCH-VILLARONGA, E. (eds.). (2022). Law and Artificial Intelligence. Springer.
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DIMATTEO, L., A., PONCIBO, C., CANNARSA, M. (eds.). (2022). The Cambridge Handbook of Artificial Intelligence. Cambridge University Press.
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RUSSELL, S., NORVIG, P. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.
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