Lecture note on Artificial Intelligence

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

208 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 Lecture Notes

This book explains the following topics: Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning, Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

Author(s): Chuck Dyer

NA 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

Artificial Intelligence Techniques Notes

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.

Author(s): Dr John A. Bullinaria

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

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

NA Pages

Artificial Intelligence II (David Marshall)

AI is the part of computer science concerned with designing intelligent computer systems, that is, computer systems that exhibit the characteristics we associate with intelligence in human behaviour - understanding language, learning, reasoning and solving problems .A theme we will develop in this course note is that most AI systems can broken into: Search, Knowledge Representation and applications of the above.

Author(s): David Marshall

NA Pages

Machine Learning, Neural and Statistical Classification

Currently this section contains no detailed description for the page, will update this page soon.

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Building Expert Systems In Prolog

Currently this section contains no detailed description for the page, will update this page soon.

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Pages