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 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 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 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 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 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