Machine Learning, Neural and Statistical Classification (D. Michie, D. Spiegelhalter, C. Taylor)
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Machine Learning, Neural and Statistical Classification (D. Michie, D. Spiegelhalter, C. Taylor)
Machine Learning, Neural and Statistical Classification (D. Michie, D. Spiegelhalter, C. Taylor)
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
This note explains the following topics:
Problem Solving by Search , Knowledge and Reasoning, Planning Classical
Planning, Uncertain knowledge and Learning.
Author(s): Department of CSE, Sri
Indu College of Engineering and Technology
This note describes the following topics: introduction to AI and production systems,
Representation of Knowledge, Knowledge Representation using predicate logic,
Knowledge Inference, Planning and Machine Learning, Expert Systems and Meta
Knowledge.
Author(s): St Anne College of Engineering and
Technology
This
note provides an introduction to the field of artificial intelligence. Major
topics covered includes: reasoning and representation, search, constraint
satisfaction problems, planning, logic, reasoning under uncertainty, and
planning under uncertainty.
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 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 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.