Cybernetics and Artificial Intelligence
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BE5B33KUI | Z,ZK | 6 | 2P+2C | English |
- Corequisite:
- The course cannot be taken simultaneously with:
- Cybernetics and Artificial Intelligence (AE3B33KUI)
Cybernetics and Artificial Intelligence (A3B33KUI)
Cybernetics and Artificial Intelligence (B3B33KUI) - The course is a substitute for:
- Cybernetics and Artificial Intelligence (AE3B33KUI)
Cybernetics and Artificial Intelligence (A3B33KUI)
Cybernetics and Artificial Intelligence (B3B33KUI) - Garant předmětu:
- Tomáš Svoboda
- Lecturer:
- Petr Pošík, Tomáš Svoboda
- Tutor:
- Filipe Gama, Jana Kostlivá, Petr Pošík, Tomáš Svoboda
- 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 2022/2023:
- Time-table is not available yet
- Time-table for summer semester 2022/2023:
-
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)