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:
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 book covers recent advances of machine learning techniques in a
broad range of applications in smart cities, automated industry, and emerging
businesses.
This note covers the
following topics: Problem Solving by Search, Informed State Space Search,
Propositional Logic, Informed State Space Search, AND/OR Graphs and Game Trees,
Method of Resolution Refutation, GraphPLAN and SATPlan, Reasoning under
Uncertainty, Learning Decision Trees, Convolutional and Recurrent Neural
Networks.
Author(s): Prof.
Pallab Dasgupta and Prof. Partha Pratim Chakrabarti
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 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 provides an introduction to artificial intelligence.
Topics covered include: representation and inference in first-order logic,
modern deterministic and decision-theoretic planning techniques, basic
supervised learning methods, and Bayesian network inference and learning.
Author(s): Prof. Tomas Lozano-Perez and Prof.
Leslie Kaelbling
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