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Course info
USII / EUVI2
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Course description
Department/Unit / Abbreviation
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USII
/
EUVI2
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Academic Year
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2020/2021
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Academic Year
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2020/2021
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Title
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Artificial and Computat. Intelligence II
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Form of course completion
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Examination
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Form of course completion
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Examination
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Long Title
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Artificial and Computational Intelligence II
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Accredited / Credits
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Yes,
5
Cred.
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Type of completion
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Written
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Type of completion
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Written
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Time requirements
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Lecture
2
[HRS/WEEK]
Tutorial
2
[HRS/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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No
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Included in study average
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YES
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Language of instruction
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English
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Occ/max
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|
|
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / 12
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0 / 8
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0 / 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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not determined
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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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English
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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 |
A|B|C|D|E|F |
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 |
Yes
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Fundamental theoretical course |
No
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Evaluation scale |
A|B|C|D|E|F |
Evaluation scale for credit before examination |
S|N |
Substituted course
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USII/FUVI2
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Preclusive courses
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N/A
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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
,
XLS
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Course objectives:
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The aim of the course is to acquaint students with advanced approaches to artificial and computational intelligence and possibilities of their application in the design of intelligent systems.
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Requirements on student
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Assignment: participation in exercises (see directive), written tests throughout the semester with a success rate of at least 60 %, completion and successful defence of two projects.
The examination is written with a success rate of at least 60 %.
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Content
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Biological foundations of artificial and computational intelligence methods.
Fuzzy set, linguistic variable, membership function.
Fuzzy logic and fuzzy resolution principle.
Fuzzy inference systems.
Fuzzy expert systems and Fuzzy Prolog.
Bayesian networks.
Models of neural networks and approaches to their learning.
Recurrent neural networks.
Deep neural networks.
Neural networks with unsupervised learning.
Introduction to evolutionary stochastic optimization algorithms, genetic algorithm.
Genetic programming, differential evolution, swarm intelligence.
Hybrid systems - fuzzy logic neural networks, evolutionary neural networks and evolutionary fuzzy systems.
Knowledge and expert knowledge base design.
Programming knowledge and expert systems.
An example of knowledge management and decision making systems.
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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:
NEGNEVITSKY, M. Artificial intelligence: A guide to intelligent systems. Harlow: Pearson Education, 2011. ISBN 9781408225745.
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Basic:
KRUSE, R. a kol. Computational intelligence: A methodological introduction. London: Springer, 2013. ISBN 978-1-4471-5849-3.
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Basic:
ENGELBRECHT, A. P. Computational intelligence: An introduction. Chichester: John Wiley & Sons, 2007. ISBN 978-0470035610.
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Recommended:
RUTKOWSKI, L. Computational intelligence: Methods and Techniques. Berlin: Springer Verlag, 2008. ISBN 978-3-540-76287-4.
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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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Domácí příprava na výuku
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28
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Příprava na zápočet
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10
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Kontaktní výuka
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52
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Individual project
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30
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Příprava na zkoušku
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30
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Total
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150
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Prerequisites - other information about course preconditions |
- |
Competences acquired |
A student who has successfully completed the course can:
explain the biological foundations of artificial and computational intelligence methods;
characterize and distinguish probability and fuzzy approaches to uncertainty processing;
describe the structure and behavior of fuzzy inference systems;
categorize and compare different models of neural networks;
characterize evolutionary stochastic optimization algorithms and distinguish individual algorithms;
compare the advantages and disadvantages of artificial and computational intelligence methods.
A student who has successfully completed the course can:
derive knowledge using inference rules of propositional fuzzy logic and first order predicate fuzzy logic;
design a fuzzy expert system knowledge base in a logic programming language;
design a suitable neural network structure for a given task and teach it on preprocessed structured and unstructured data;
design a purpose function and optimize the task using evolutionary stochastic optimization algorithms;
design a hybrid intelligent system that takes advantage of the combination of different methods of artificial and computational intelligence.
The student who has successfully completed the course is able to:
decide independently and responsibly on the basis of a framework assignment and take into account the complexity, constraints and uncertainties associated with that decision;
acquire additional expertise, skills and competences in related disciplines;
communicate in a clear and convincing way their own professional opinions to public administration professionals and to the wider public. |
Teaching methods |
- Monologic (reading, lecture, briefing)
- Dialogic (discussion, interview, brainstorming)
- Laboratory work
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Assessment methods |
- Oral examination
- Written examination
- Didactic test
- Discussion
- Class observation
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