2-IKV-133 Fundamentals of Artificial Intelligence (2)
Evaluation during the course: project, interim project evaluation
Concluding evaluation: final test + project evaluation
Subject aim: Provide knowledge from artificial intelligence: complex decision making,
learning, communication and language.
Brief curriculum of the subject:
Probabilistic
language processing
- PCFG, IR
systems
- machine
translation: language model, translation
model
Decision
making
- Markov
decision processes (MDP), iteration of values, iteration of strategies
- partially
observable Markov decision processes (POMDP).
Learning
- EBL, KBL,
KBIL
- decision
trees, ID3 algorithm
Statistical
learning
- discrete
models, continuous models, learning the structure of Bayesian nets
-
expectation-maximization (EM) algorithm
-
clustering, learning the structure of Bayesian nets with hidden parameters
- passive
reinforcement learning (direct utility estimation, ADP, TD learning)
- active
reinforcement learning (exploration, action value function)
Literature:
Language in which the subject is
taught: Slovak
Date of the last sheet revision: 9.8.2007