Introduction to Artificial Intelligence by Thomas P Trappenberg
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Introduction to Artificial Intelligence by Thomas P Trappenberg
Introduction to Artificial Intelligence by Thomas P Trappenberg
This note covers introduction and history, Search, Robotics
and motion planning, Constraint satisfaction problem, Machine learning, Learning
machines and the perceptron, Regression, classification and maximum likelihood,
Support vector machines, Learning Theory, Generative models and Naïve Bayes,
Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian
networks and Markov models.
Author(s): Thomas P Trappenberg,
Dalhousie University
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 covers introduction and history, Search, Robotics
and motion planning, Constraint satisfaction problem, Machine learning, Learning
machines and the perceptron, Regression, classification and maximum likelihood,
Support vector machines, Learning Theory, Generative models and Naïve Bayes,
Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian
networks and Markov models.
Author(s): Thomas P Trappenberg,
Dalhousie University
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 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 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 lecture note
covers the following topics: Introduction to Agent, Problem Solving using
Search, State Space Search, Pegs and Disks problem, Uninformed Search , Single
agent search, Informed Search Strategies, Two agent, Constraint satisfaction
problems, Knowledge Representation and Logic, First Order Logic, Rule based
Systems, Other representation formalisms, Planning, Reasoning with Uncertainty -
Probabilistic reasoning, Reasoning with uncertainty-Fuzzy Reasoning.