Artificial Intelligence 460-KS1-2SIN3
Study profile: general
Study form: full-time
Type of course: obligatory
Scientific domain / discipline: humanities / philosophy, social communication and media
Year/semester: 2/3
Lecture: 15 h, Laboratory: 15 h
ECTS: 2
Balance of a student's labour input:
participation in lectures -- 15 h
participation in lab classes -- 15 h
individual consulting with teachers -- 2 h
preparation to classes: laboratory 10 h, lecture 5 h
preparation to passing of the lab classes -- 5 h
preparation to passing of the lecture -- 10 h
examination 2 h
Activities requiring of a direct participation of the teacher: 34 h, 1 ECTS
Activities not requiring of a direct participation of the teacher: 30 h, 1 ECTS
Course coordinators
Type of course
Mode
Learning outcomes
The graduate knows and understands:
- basic information technologies used to support cognitive and communication processes -- KA6_WG2 (lecture, lab);
- methods of artificial intelligence used in modelling and analysis of cognitive and communication systems -- KA6_WG3 (lecture, lab);
- ethical and philosophical problems of AI -- KA6_WK6 (lecture).
The graduate knows how:
- to formulate a cognitive research problem and to propose its solution in the form of scientific hypothesis -- KA6_UW1 (lecture, lab);
- to use information technologies and tools for information retrieval and support of cognitive and communication processes -- KA6_UW7 (lab).
The graduate is prepared:
- to be open to new trends in the contemporary science and society and to ask for experts' opinions on solving cognitive and practical problems -- KA6_KK2 (lecture, lab);
- to improve the professional skills and to raise the personal competences, the ethical ones including -- KA6_KR1 (lecture, lab);
- to work in teams and to play various roles in these teams -- KA6_KR2 (lab).
The methods of verification of the achievements: a written test, a written elaboration of tasks, laboratory tasks, observation of student's progress during lab classes, discussion.
Assessment criteria
Methods of teaching / learning: lectures with use of multimedial presentations, solving of tasks and problems, laboratory classes, discussion.
Passing of the lecture on the basis of written or oral examination in the form of a test or elaboration of tasks. Passing of the laboratory on the basis of written tasks and/or project tasks, and observation of a student's progress in work at classes.
The necessary condition for the positive passing of the subject is obtaing at least 3.0 both for the laboratory and the lecture.
In the case of absence, students have to catch up on classes themselves.
Bibliography
1. Flasiński, M., Introduction to Artificial Intelligence, Springer Verlag, Switzerland 2016.
2. Russell, S. J., Norvig, P., Artificial Intelligence: A Modern Approach, 3rd Ed., Pearson, 2014.
3. Rutkowski, L., Computational Intelligence. Methods and Techniques, Springer-Verlag, Berlin Heidelberg 2008.
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Term 2023:
1. Flasiński, M., Introduction to Artificial Intelligence, Springer Verlag, Switzerland 2016. |
Term 2024:
1. Flasiński, M., Introduction to Artificial Intelligence, Springer Verlag, Switzerland 2016. |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: