Cybernetics and Artificial Intelligence

The course is a substitute for:

Cybernetics and Artificial Intelligence (E33KUI)
Cybernetics and Artificial Intelligence (X33KUI)

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

A general course enabling to understand the goals and principles of cybernetics and artificial intelligence and to organize different topics studied in the branch of study within a unified framework. The course covers the main principles of dynamic systems, entropy, information theory, algorithmic decidability, AI techniques for problem solving, statistical decision theory, machine learning and formal knowledge representation. The unifying conceptual approach to many diverse parts of cybernetics and artificial intelligence is the key feature of this course.

Requirements:

Class requirements, basic info and link to the web page with course materials: https://cw.felk.cvut.cz/doku.php/courses/a3b33kui/start

Syllabus of lectures:

1. Introduction to Cybernetics, System Dynamics

2. Entropy and Information

3. Information Transmission, Coding

4. Algorithmic Entropy, Decidability

5. Problem Solving as Search

6. A* Algorithm

7. Stochastic Search

8. Probabilistic Decision Making and Classification

9. Two player games

10. Machine Learning

11. Knowledge Representation

12. Logic Resolution

13. (no lecture this week)

14. Written Exam

Syllabus of tutorials:

1. A visit to the Gerstner Lab and the Center for Machine Perception at the Department of Cybernetics

2-3/Seminar: Probability, Entropy

2-3/Computer Lab: Dynamic Systems

4-5/Seminar: Information Transmission

4-5/Computer Lab: Compression Algorithms

6-7/Seminar: Search

6-7/Computer Lab: Search

8-9/Seminar: Search

8-9/Computer Lab: Search

9-10/Seminar: Classification, Learning

9-10/Computer Lab: Classification, Learning

12-13/Seminar: Classification, Learning

12-13/Computer Lab: Classification, Learning

14: Class Credits

Study Objective:

Study materials:

- Rich, E., Knight, K.: Artificial Intelligence. Mc-Graw Hill, 1991

- W. R. Ashby: An Introduction To Cybernetics, available at http://pespmc1.vub.ac.be/books/IntroCyb.pdf

- R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (2nd ed.), John Wiley and Sons, 2001.

- T. Mitchell: Machine Learning, McGraw Hill, 1997.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Cybernetics and Measurements- structured studies (compulsory course)
• Computer Technology- structured studies (compulsory elective course)