Subject description - AE3B33KUI
Summary of Study |
Summary of Branches |
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List of Roles |
Explanatory Notes
Instructions
AE3B33KUI | Cybernetics and Artificial Intelligence | ||
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Roles: | P, V | Extent of teaching: | 2P+2C |
Department: | 13133 | Language of teaching: | EN |
Guarantors: | Completion: | Z,ZK | |
Lecturers: | Credits: | 5 | |
Tutors: | Semester: | L |
Anotation:
The course will enable students to understand the basic concepts, goals and methods of cybernetics and artificial intelligence, and align some individual topics studied in the bachelor stage into the more profound context of the study program. The syllabus contains topics concerned with general aspects of systems and information theory, problem solving and state space search principles, elements of game theory, knowledge and expert systems, elements of decision theory, recognition and machine learning. The most important feature of the course is its unifying conceptual approach to many, at first sight diverse, components of cybernetics and aritifical intelligence.Study targets:
The course will enable students to understand the basic concepts, goals and methods of cybernetics and artificial intelligence, and align some individual topics studied in the bachelor stage into the more profound context of the study program.Course outlines:
Introduction to cybernetics, systems and models Elements of general systems theory Information, entropy, information transmission, coding - a cybernetic view Algorithmic entropy, decidability Problem solving, the resolution principle Search algorithms, stochastic search Game theory, two-player games Knowledge representation, semantic networks, production systems, frames and scenarios Expert systems, their architecture, uncertain information processing models Decision and classification principles, Bayesian decision making, attributes, attribute space, recognition, cluster analysis Structural recognition, relations to machine perception and image/scene analysis Neural networks and their training, genetic and evolutionary algorithms Machine learning Applications (if timetable allows)Exercises outline:
1. | - | 2. Cybernetic systems lab showcase |
2. | - | 4. Seminar: Probability and entropy |
3. | - | 4. Computer lab: System models |
5. | - | 6. Seminar: Information transmission |
5. | - | 6. Computer lab: Compression algorithms |
6. | - | 10. Seminar: Search |
7. | - | 10. Computer lab: Search |
8. | - | 12. Seminar: Decision making, classification, recognition |
9. | - | 12. Computer lab: Expert systems |
10. | - | 13. Seminar with computer simulation: Evolutionary algorithms, neural networks |
11. | - | 14. Machine learning, class credits |
Literature:
Nilsson, N. N.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publ. San Francisco, 1998Requirements:
https://cw.felk.cvut.cz/doku.php/courses/a3b33kui/startWebpage:
http://cw.felk.cvut.cz/doku.php/courses/ae3b33kui/startKeywords:
Cybernetics, artificial intelligence Subject is included into these academic programs:Program | Branch | Role | Recommended semester |
BEKME1 | Communication Technology | V | 4 |
BEKME5 | Komunikace a elektronika | V | 4 |
BEKME_BO | Common courses | V | 4 |
BEKME4 | Network and Information Technology | V | 4 |
BEKME3 | Applied Electronics | V | 4 |
BEKME2 | Multimedia Technology | V | 4 |
BEKYR1 | Robotics | P | 2 |
BEKYR_BO | Common courses | P | 2 |
BEKYR3 | Systems and Control | P | 2 |
BEKYR2 | Sensors and Instrumentation | P | 2 |
BEEEM1 | Applied Electrical Engineering | V | 4 |
BEEEM_BO | Common courses | V | 4 |
BEEEM2 | Electrical Engineering and Management | V | 4 |
BEOI1 | Computer Systems | V | 4 |
BEOI_BO | Common courses | V | 4 |
BEOI3 | Software Systems | V | 4 |
BEOI2 | Computer and Information Science | V | 4 |
Page updated 5.3.2021 17:52:09, semester: Z/2020-1, L/2021-2, L/2020-1, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |