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Advanced AI Techniques
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WS
2005
Advanced AI Techniques
1.
Bayesian Networks
1.1.
Learning Bayesian Networks
1.2.
Probabilistic Independence and Separation in Graphs
1.3.
Parameter Learning
1.4.
Parameter Learning with Missing Values; Structure Learning (1)
1.5.
Structure Learning (2)
2.
Statistical NLP
2.1.
Introduction and Grammar Models; Makrov Models and N-gramms (1)
2.2.
Markov Models and N-gramms (2)
2.3.
Hidden Markov Models (1)
2.4.
Hidden Markov Models (2)
2.5.
Hidden Markov Models (3); Probabilistic Context Free Grammars (1)
2.6.
Probabilistic Context Free Grammars (2)
2.7.
Probabilistic Context Free Grammars (3); Applications of HMMs and PCFGs in NLP (1)
2.8.
Applications of HMMs and PCFGs in NLP (2); Probabilistic DCGs and SLPs; Conditional Random Fields
3.
Reinforcement Learning
3.1.
Reinforcement Learning (1)
3.2.
Reinforcement Learning (2)
3.3.
Reinforcement Learning (3)
3.4.
Reinforcement Learning (4)
3.5.
Reinforcement Learning (5)
4.
Bayes Filtering
4.1.
Bayes Filtering (1)
4.2.
Bayes Filtering (2)
5.
Distributed AI and Multi-Agent Systems
5.1.
Distributed AI and Multi-Agent Systems (1)
5.2.
Distributed AI and Multi-Agent Systems (2)
6.
An Introduction to Game Theory
6.1.
Strategic Games (1)
6.2.
Strategic Games (2)
6.3.
Mixed Strategies
6.4.
Strictly Competive Games (1)
6.5.
Strictly Competive Games (2)
6.6.
Games with Imperfect Information
6.7.
Extensive Games with Perfect Information
6.8.
Extensive Games with Perfect Information 2
6.9.
Repeated Games: The Prisoner's Dilemma Revisited
All electures of chapter 'Strategic Games (1)'
Title
Lecturer(s)
Rating
Comments
Strategic Games (1)
Prof. Dr. Bernhard Nebel
Currently 3.1666667/5 Stars.
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