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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 1 Computational Learning Theory.
Documents Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 1 Computational Learning Theory.

Slide 1Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 1 Computational Learning Theory Slide 2 Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning…

Learning Dimensions: What can be learned? --Any of the boxes representing the agent’s knowledge --action description, effect probabilities, causal relations.
Documents Learning Dimensions: What can be learned? --Any of the boxes representing the agent’s knowledge...

Slide 1 Learning Dimensions: What can be learned? --Any of the boxes representing the agent’s knowledge --action description, effect probabilities, causal relations in…

1 Chapter 2 - Concept learning Learning from examples Concept Learning General-to specific ordering of hypotheses Version spaces and candidate.
Documents 1 Chapter 2 - Concept learning Learning from examples Concept Learning General-to specific...

Slide 1 1 Chapter 2 - Concept learning  Learning from examples  Concept Learning  General-to specific ordering of hypotheses  Version spaces and candidate elimination…

Knowledge discovery & data mining: Classification UCLA CS240A Winter 2002 Notes from a tutorial presented @ EDBT2000 By Fosca Giannotti and Dino Pedreschi.
Documents Knowledge discovery & data mining: Classification UCLA CS240A Winter 2002 Notes from a tutorial...

Slide 1 Knowledge discovery & data mining: Classification UCLA CS240A Winter 2002 Notes from a tutorial presented @ EDBT2000 By Fosca Giannotti and Dino Pedreschi Pisa…

Bayesian models of human inductive learning Josh Tenenbaum MIT.
Documents Bayesian models of human inductive learning Josh Tenenbaum MIT.

Slide 1 Bayesian models of human inductive learning Josh Tenenbaum MIT Slide 2 Everyday inductive leaps How can people learn so much about the world from such limited evidence?…

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