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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BI-ZNS Knowledge-based Systems Extent of teaching: 2P+2C
Instructor: Jiřina M. Completion: Z,ZK
Department: 18105 Credits: 5 Semester: Z

Annotation:
Students will become familiar with the systems based on knowledge (knowledge-based systems), which are systems that usetechniques of artificial intelligence to solve problems that require human judgment, learning and reasoning from findingsand actions. The course introduces students to the philosophy and architecture of knowledge-based systems to support decision-makingand planning. The course assumes knowledge of set theory, probability theory, artificial neural networks, and evolutionary algorithms.

Lecture syllabus:
1. Introduction to knowledge-based systems.
2. Knowledge-based system architecture, knowledge representation.
3. Inference mechanism, methods for realization of inference mechanism.
4. Expressing and processing uncertainty.
5. Creation of knowledge-based system, ontology, knowledge acquisition.
6. Bayesian networks (example of a calculation).
7. Multivalued logic, fuzzy logic, operations in fuzzy logics.
8. Rule inference fuzzy system.
9. Knowledge representation using decision trees.
10. Neural networks and their use for knowledge representation and rule inferencing.
11. Extraction of rules from decision trees.
12. Extraction of rules from neural networks.
13. Application of rules in multiagent systems.

Seminar syllabus:
1. Introductory exercise, familiarization with evaluation rules and the framework for tasks.
2. Knowledge representation. Assignment and work on the 1st task.
3. Submission of the 1st task.
4. Inference and explanatory mechanism. Assignment and work on the 2nd task.
5. Submission of the 2nd task.
6. Uncertainty. Assignment and work on the 3rd task.
7. Submission of the 3rd task.
8. Fuzzy logic. Assignment and work on the 4th task.
9. Extraction of rules 1
10. Submission of the 4th task.
11. Neural networks
12. Extraction of rules 2
13. Submission of the final task and granting credits.

Literature:
[1] Akerkar, R. - Sajja, P.: Knowledge-Based Systems, Jones &; Bartlett Learning, 2009, 0763776475,
[2] Kendal, S. - Creen, M.: An Introduction to Knowledge Engineering, Springer, 2006, 1846284759,
[3] Brachman, R. - Levesque, H.: Knowledge Representation and Reasoning, Morgan Kaufmann, 2004, 1558609326,

Requirements:
Basic knowledge of mathematical logic, probability and statistics.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/BI-ZNS/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
BI-PI.2015 Computer engineering V 5
BI-WSI-PG.2015 Web and Software Engineering V 5
BI-WSI-SI.2015 Web and Software Engineering V 5
BI-ISM.2015 Information Systems and Management PO 5
BI-BIT.2015 Computer Security and Information technology V 5
BI-ZI.2018 Knowledge Engineering PO 3
BI-SPOL.2015 Unspecified Branch/Specialisation of Study VO 5
BI-WSI-WI.2015 Web and Software Engineering V 5
BI-TI.2015 Computer Science V 5


Page updated 29. 3. 2024, semester: L/2021-2, Z,L/2023-4, Z/2021-2, Z/2020-1, Z/2019-20, L/2020-1, Z,L/2022-3, L/2019-20, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška