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Course info
KKY / UUI
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Course description
Department/Unit / Abbreviation
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KKY
/
UUI
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Introduction to Artificial Intelligence
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
5
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
2
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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KKY/UI
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The course explains a basic approaches used in artificial intelligence at solution of cybernetic tasks. The predominant part of the course will be aimed at problem solving, computer games and knowledge representation and reasoning.
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Requirements on student
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Activity at seminars. Solution of AI tasks on PC, understanding of basic themes of lectured matter.
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Content
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1.-3. The use of artificial intelligence (AI) techniques in cybernetics. The basic approaches of AI - machine recognition, machine learning, machine understanding, machine perception etc. 4.-5. Problem-solving, backtracking, breadth-first search, depth-first search. Informed strategies, A star strategy, heuristic function, hill climbing search, Beam, Branch & Bound. Handling special constrains. 6. Game playing, Minimax algorithm, alpha-beta pruning. Examples. 7. Knowledge representation and reasoning. Statement logic; inference in statement logic, resolution. Examples. 8. First-order logic, basic terminology; natural language and first-order logic, conversion of natural language sentences into the first-order logic formulas. Examples. 9.-10. Inference in first-order logic, unification, conjunctive normal form for firts-order logic. The resolution inference rule. Resolution strategies. Prolog. 11. Production systems. Structure of production system, base of knowledge, production rules. Control strategies, bottom-up and bottom-down strategy. Examples 12. Representation of knowledge by semantic nets, frames and scenarios. Examples 13. Conclusion of the course.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
Russell, Stuart J.; Norvig, Peter. Artificial intelligence : a modern approach. Upper Saddle River : Prentice Hall, 2003. ISBN 0-13-790395-2.
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Basic:
Psutka, Josef; Kepka, Jiří. Umělá inteligence reprezentace znalostí. Plzeň : ZČU, 1994. ISBN 80-7082-126-4.
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Basic:
Mařík, Vladimír. Umělá inteligence (1). Academia, Praha, 1993. ISBN 80-200-0496-3.
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Basic:
Mařík, Vladimír a kol. Umělá inteligence (2). Academia, Praha, 1997.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Preparation for an examination (30-60)
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50
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Preparation for comprehensive test (10-40)
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20
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Individual project (40)
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15
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Contact hours
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52
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Total
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137
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
disponovat základními znalostmi matematické analýzy (KMA/M1) |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
srozumitelně formulovat problém |
používat aktivně základní znalosti matematické analýzy |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
formulovat a řešit jednoduché problémy z oblasti řešení úloh (problem solving) |
reprezentovat znalosti logickými a relačními formalismy a ozumět a používat základní metody odvozování znalostí v UI |
řešit jednoduché úlohy hraní her pro 2 hráče |
Skills - skills resulting from the course: |
v oblasti praktického řešení úloh (problem solving); dovede formalizovat stavy a operátory, dovede vybrat strategii řešení;
v oblasti strojové reprezentace znalostí student dovede dle povahy úlohy volit mezi logickými a relačními formalismy reprezentace znalostí; dovede pracovat s odvozovacími formalismy |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Skills demonstration during practicum |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Skills demonstration during practicum |
Odborné dovednosti jsou získávány postupně, a to řešením praktických úloh na seminářích, neformální diskusí vedenou k řešenému problému, analýzou závěrů. |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Skills demonstration during practicum |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Seminar |
Individual study |
Skills - the following training methods are used to achieve the required skills: |
Lecture |
Seminar |
Task-based study method |
Skills demonstration |
Competences - the following training methods are used to achieve the required competences: |
Lecture |
Task-based study method |
Seminární cvičení a též zkouška jsou platformou k demonstraci cílených způsobilostí. |
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