Artificial Intelligence 500-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
Didactic methods: lecture, laboratory, realization of a project with a report, individual consulting with teachers.
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 the classes: lecture -- 3 h, laboratory -- 7 h
preparation to passing of the lab classes -- 10 h
preparation to passing of the lecture -- 10 h
passing of the lecture -- 2 h
Quantitative indices:
activities requiring of a direct participation of the teacher: 34 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 -- KP6_WG2 (lecture, lab);
- methods of artificial intelligence used in modelling and analysis of cognitive and communication systems -- KP6_WG3 (lecture, lab).
The graduate can use information technologies and tools for information retrieval and support of cognitive and communication processes -- KP6_UW7 (lab).
The graduate is prepared:
- to adopt a critical attitude in scientific discussions and to accept the importance of knowledge and rational argumentation -- KP6_KK1 (lecture);
- to be open to new trends in the field of artificial intelligence and to ask for experts' opinions on solving cognitive and practical problems -- KP6_KK2 (lecture, lab);
- to choose methods appropriate for realization of practical, cognitive, and communication tasks -- KP6_KR3 (lab).
The methods of verification of the achievements: a written test, a written elaboration of tasks, laboratory tasks, observation during classes.
Assessment criteria
Methods of teaching / learning: lecture, solving of tasks and problems, laboratory classes, individual consulting with the teacher.
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, project tasks with reports, elaboration of topics, observation during classes.
The necessary condition for the positive passing of the subject is obtaining 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.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: