This note explains the
following topics: Search, Game playing, Logic, Planning, Probabilistic
reasoning, Decision theory, Markov decision processes, POMDPs, Game theory,
Machine learning, Wrapping up.
This book
explains the following topics: Principles of knowledge-based search techniques,
automatic deduction, knowledge representation using predicate logic, machine
learning, probabilistic reasoning, Applications in tasks such as problem
solving, data mining, game playing, natural language understanding, computer
vision, speech recognition, and robotics.
This note explains the
following topics: Search, Game playing, Logic, Planning, Probabilistic
reasoning, Decision theory, Markov decision processes, POMDPs, Game theory,
Machine learning, Wrapping up.
This note explains the following topics: State Space
Search, Decision Trees, Evaluating Hypotheses, Evaluation of hypothesis, Neural
Networks, Computational Learning Theory, DMF Clustering, Data Mining, Text
Mining, Graph Mining, Text Mining.
This note provides a
general introduction to artificial intelligence and its techniques. Topics
covered includes: Biological Intelligence and Neural Networks, Building
Intelligent Agents, Semantic Networks, Production Systems, Uninformed Search,
Expert Systems, Machine Learning, Limitations and Misconceptions of AI.
This note is
designed as a broad rather than in-depth introduction to the principles of
artificial intelligence, its characteristics, major techniques, and important
sub-fields and applications.
This course note introduces representations,
techniques, and architectures used to build applied systems and to account for
intelligence from a computational point of view.
Author(s): Prof. Leslie Kaelbling and
Prof. Tomas Lozano-Perez
This course note
covers major topics of AI, including Search, Logic and Knowledge Representation,
and Natural Language Processing, with brief coverage of the Brain and Machine
Vision.
This
book is for both professional programmers and home hobbyists who already
know how to program in Java and who want to learn practical Artificial
Intelligence (AI) programming and information processing techniques. Topics
covered includes: Search, Reasoning, Semantic Web, Expert Systems, Genetic
Algorithms, Neural Networks, Machine Learning with Weka, Statistical Natural
Language Processing.