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Course info KIP / QDUIT : Course description

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Department/Unit / Abbreviation	KIP / QDUIT			Academic Year	2023/2024
Academic Year	2023/2024
Title	Artificial Intelligence			Form of course completion	Exam
Form of course completion	Exam
Accredited / Credits	Yes, 10 Cred.			Type of completion	Combined
Type of completion	Combined
Time requirements	lecture 1 [Hours/Week] seminar 1 [Hours/Week]			Course credit prior to examination	Yes
Course credit prior to examination	Yes
Automatic acceptance of credit before examination	No
Included in study average	YES
Language of instruction	-
Occ/max				Automatic acceptance of credit before examination	No
Summer semester	0 / -	1 / -	0 / -	Included in study average	YES
Winter semester	0 / 0	0 / -	0 / 0	Repeated registration	NO
Repeated registration	NO
Timetable	Yes			Semester taught	Winter + Summer
Semester taught	Winter + Summer
Minimum (B + C) students	not determined			Optional course	No
Optional course	No
Language of instruction	-			Internship duration	0
No. of hours of on-premise lessons				Evaluation scale	A|B|C|D|E|F
Periodicity	every year			Evaluation scale for credit before examination	S|N
Specification periodicity				Fundamental theoretical course	No
Fundamental course	Yes
Fundamental theoretical course	No
Evaluation scale	A|B|C|D|E|F
Evaluation scale for credit before examination	S|N
Substituted course	None
Preclusive courses	N/A
Prerequisite courses	N/A
Informally recommended courses	N/A
Courses depending on this Course	N/A
Histogram of students' grades over the years: Graphic PNG ,  XLS

Course objectives:
Poskytnout studentům doktorských studijních programů širší pohled na oblast umělé inteligence.
Requirements on student
Vypracovat a obhájit semestrální práci. Tato aktivita je ohodnocena maximálně 60 body. Ústní část zkoušky je pak hodnocena 40 body.
Content
1. Moderní trendy umělé inteligence. 2. Umělý život a automaty. 3. Multiagentní systémy. 4. Strojové učení. 5. Reprezentace znalostí v UI a klasifikace. 6. Expertní systémy. 7. Robotika. 8. Řešení úloh v UI. Jednotlivá témata budou specifikována s ohledem na náplň disertační práce.
Activities
Fields of study
Guarantors and lecturers
Guarantors: doc. RNDr. Martin Kotyrba, Ph.D. (100%),  Lecturer: doc. RNDr. Martin Kotyrba, Ph.D. (100%),  Seminar lecturer: doc. RNDr. Martin Kotyrba, Ph.D. (100%),
Literature
Basic: Martin Kotyrba. Umělá inteligence - Rozpoznávání vzorů v dynamických datech. Praha, 2014. ISBN 978-80-7300-497-2. Extending: Stuart Russell. Artificial Intelligence: A Modern Approach. England, 2015. ISBN 978-1-292-15396-4. Recommended: Jerry Kaplan. Artificial Intelligence: What Everyone Needs to Know. England, 2016. ISBN 9780190602390. Recommended: Mařík, V. a kol. Umělá inteligence, díl IV.. ACADEMIA Praha, 2003. ISBN 80-200-1044-0. Recommended: Mařík, V. a kol. Umělá inteligence, díl V.. ACADEMIA Praha, 2007. ISBN 978-80-200-1470-2. On-line library catalogues
Time requirements
All forms of study Activities Time requirements for activity [h] Presentation (of works, projects, etc.) 30 Self-tutoring 100 Preparation for an exam 70 Consultation of work with the teacher/tutor (incl. electronic) 40 Semestral work 60 Total 300
Prerequisites
Competences - students are expected to possess the following competences before the course commences to finish it successfully: Bez předpokladů.
Learning outcomes
Knowledge - knowledge resulting from the course: Student umí: - aplikovat vybrané metody umělé inteligence při řešení úloh. Skills - skills resulting from the course: Student zná: - implementovat vybrané metody z oblasti umělé inteligence.
Assessment methods
Knowledge - knowledge achieved by taking this course are verified by the following means: IC6 - Oral examiantion IIA6 - Project (outcome of project education)
Teaching methods
Knowledge - the following training methods are used to achieve the required knowledge: B1 - Discussion F7 - Creation of expert documentation G2 - Self-study, controlled study