Artificial Intelligence 510-IS1-3SZI-23
Study profile: general academic
Study form: full-time
Type of course: obligatory
Scientific domain: exact and natural sciences; discipline: computer science
Year/semester: 3/6
Lecture: 30h, Laboratory: 30h
Didactic methods: lecture, laboratory, realization of a project with a report, individual consulting with teachers.
ECTS: 4
Balance of a student's labour input:
Participation in classes:
- lecture 30h
- laboratory 30h
Preparation to classes:
- Preparation to classes: lecture - 2h, laboratory - 10h
- Getting acquainted with the literature: 10h
- Reports and other: 10h
- Preparation to the exam: 8h
Duration of the exam: 2h
Duration of the passing: 2h
Individual consulting with teachers: 5h
Quantitative indices:
- a student's workload related to the activities requiring of the direct participation of teachers: 69h, 2.7 ECTS,
- the ECTS points obtained by activities relevant to the research in computer science conducted at the university: 4 ECTS.
Type of course
Course coordinators
Mode
Learning outcomes
Knowledge:
A graduate knows selected problems of artificial intelligence, knowledge representation and processing, and communication man -- machine at an advanced level, KP6_WG11.
Skills: A graduate can
- describe problems expressed in natural language using an artificial intelligence terminology, KP6_UW14;
- solve a problem from information sciences, partly theoretical and partly practical, and can present its solution and conclusions, KP6_UK3;
- collaborate with others on the realization of a project, KP6_UO2;
- undestand the need of improvement of their skills and competence, and follows the development of the computer science methods and technologies, KP6_UU1.
Social competence:
A graduate understands the need of obeying of the ethical and legal rules concerning the activity in the computer science society, KP6_KR1.
Assessment criteria
The general form of the credit: an exam
Bibliography
1. M. Flasiński, Introduction to Artificial Intelligence, Springer Verlag, Switzerland 2016.
2. L. Moroney, AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence, O'Reilly, 2021.
3. S.J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Ed., Pearson, 2014.
4. L. Rutkowski, 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: