Techniques of Artificial Intelligence by Vrije Universiteit Brussel
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Techniques of Artificial Intelligence by Vrije Universiteit Brussel
Techniques of Artificial Intelligence by Vrije Universiteit Brussel
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 will provide
an introduction to the field of Artificial Intelligence. It will cover a number
of AI ideas and techniques, as well as give you a brief introduction to symbolic
computing.
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 note explains artificial intelligence, including agent
design, heuristic search, knowledge representation, planning, logic, natural
language processing and machine learning.
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
based on the EC (ESPRIT) project StatLog which compare and evaluated a range of
classification techniques, with an assessment of their merits, disadvantages and
range of application. It provides a concise introduction to each method, and
reviews comparative trials in large-scale commercial and industrial problems.
Author(s): D. Michie, D.J. Spiegelhalter, C.C. Taylor