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Artificial Intelligence by Seoul National University
This book explains the following topics: History of AI, Machine Evolution, Evolutionary Computation, Components of EC, Genetic Algorithms, Genetic Programming, Uninformed Search, Search Space Graphs, Depth-First Search, Breadth-First Search, Iterative Deepening, Heuristic Search, The Propositional Calculus, Resolution in the Propositional Calculus, The Predicate Calculus, Resolution in the Predicate Calculus, Reasoning with Uncertain Information, Agent Architectures.
Author(s): Seoul National University
NA Pages 
Lecture note on Artificial Intelligence
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
173 Pages
Artificial Intelligence and Games
This PDF covers the following topics related to Artificial Intelligence and Games : AI Methods, Ways of Using AI in Games, Playing Games, Generating Content, Modeling Players, Game AI Panorama, Frontiers of Game AI Research.
Author(s): Georgios N. Yannakakis, Julian Togelius
359 Pages
Introduction to Artificial Intelligence
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.
Author(s): Vincent Conitzer
NA Pages
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.
Author(s): Vrije Universiteit Brussel
NA Pages
Artificial Intelligence by Professor Yun Peng
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.
Author(s): Professor Yun Peng
NA Pages
Techniques in Artificial Intelligence
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
NA Pages
Artificial Intelligence Lecture Notes MIT
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
NA Pages
Artificial Intelligence Lectures slides and readings
This note covers the following topics: Search, Backtracking Search, Game Tree Search, Reasoning Under Uncertainty, Planning, Decision Making under Uncertainty.
Author(s): Fahiem Bacchus
NA Pages
Building Expert Systems In Prolog (Amzi)
Currently this section contains no detailed description for the page, will update this page soon.
Author(s):
Pages
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
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