Cybernetics and Artificial Intelligence
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BE5B33KUI | Z,ZK | 6 | 2P+2C | English |
- Relations:
- It is not possible to register for the course BE5B33KUI if the student is concurrently registered for or has already completed the course B3B33KUI (mutually exclusive courses).
- During a review of study plans, the course B3B33KUI can be substituted for the course BE5B33KUI.
- During a review of study plans, the course AE3B33KUI can be substituted for the course BE5B33KUI.
- During a review of study plans, the course A3B33KUI can be substituted for the course BE5B33KUI.
- It is not possible to register for the course BE5B33KUI if the student is concurrently registered for or has already completed the course AE3B33KUI (mutually exclusive courses).
- It is not possible to register for the course BE5B33KUI if the student is concurrently registered for or has already completed the course A3B33KUI (mutually exclusive courses).
- In order to register for the course BE5B33KUI, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
- It is not possible to register for the course BE5B33KUI if the student is concurrently registered for or has previously completed the course B3B33KUI (mutually exclusive courses).
- It is not possible to register for the course BE5B33KUI if the student is concurrently registered for or has previously completed the course A3B33KUI (mutually exclusive courses).
- It is not possible to register for the course BE5B33KUI if the student is concurrently registered for or has previously completed the course AE3B33KUI (mutually exclusive courses).
- Course guarantor:
- Tomáš Svoboda
- Lecturer:
- Petr Pošík, Tomáš Svoboda
- Tutor:
- Swati Dantu, Filipe Gama, Jana Kostlivá, Petr Pošík, Tomáš Svoboda, Pavel Šindler
- Supervisor:
- Department of Cybernetics
- Synopsis:
-
The course introduces the students into the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transition. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown, which also connects the artificial intelligence and cybernetics fields. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algoritms in computer labs.
- Requirements:
-
Basic knowledge of linear algebra and programming is assumed. Experience in Python and basics of probability is an advantage.
- Syllabus of lectures:
-
What is artificial intelligence and what cybernetics.
Solving problems by search. State space.
Informed search, heuristics.
Games, adversarial search.
Making sequential decisions, Markov decision process.
Reinforcement learning.
Bayesian decision task.
Paramater estimation for probablistic models. Maximum likelihood.
Learning from examples. Linear classifier.
Empirical evaluation of classifiers ROC curves.
Unsupervised learning, clustering.
- Syllabus of tutorials:
-
Computer lab organization.
Search.
Informed search and heuristics.
Sequential decision problems.
Reinforcement learning.
Pattern Recognition.
- Study Objective:
-
The course introduces the students into the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transition. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown, which also connects the artificial intelligence and cybernetics fields. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algoritms in computer labs.
- Study materials:
-
Stuart J. Russel and Peter Norvig. Artificial Intelligence, a Modern Approach, 3rd edition, 2010
- Note:
- Further information:
- https://cw.fel.cvut.cz/wiki/courses/be5b33kui/start
- Time-table for winter semester 2024/2025:
- Time-table is not available yet
- Time-table for summer semester 2024/2025:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - The course is a part of the following study plans:
-
- Electrical Engineering and Computer Science (EECS) (compulsory elective course)
- Electrical Engineering and Computer Science (EECS) (compulsory elective course)