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Artificial Intelligence Books

There are many online resources where you can find free Artificial Intelligence books to download in PDF format, including online textbooks, ebooks, lecture notes, and more, covering basic, beginner, and advanced concepts for those looking for an introduction to the subject or a deeper understanding of it.

Digital Notes on Artificial Intelligence by Sri Indu College of Engineering and Technology

This note explains the following topics: Problem Solving by Search , Knowledge and Reasoning, Planning Classical Planning, Uncertain knowledge and Learning.

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s 140Pages

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.

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s 173Pages

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.

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s 208Pages

Digital notes on Artificial Intelligence

This lecture note covers topics starting with an introduction to AI and progressing through various search strategies and A* search. The text delves into challenges such as searching with partial observations and constraint satisfaction problems, introducing techniques like Alpha Beta Pruning. It explores reasoning methods like forward and backward chaining, syntax and semantics of First-Order Logic, knowledge engineering, resolution, classical planning, and planning with state space search.it also handles with topics like acting in nondeterministic domains and multi-agent planning, Bayes Rule, Bayesian Networks and Dempster Shafer Theory.It includes learning decision trees and the role of knowledge in learning.

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s 143Pages

Explorations in Artificial Intelligence and Machine Learning

This PDF covers the following topics related to Artificial Intelligence and Machine Learning : Introduction to Machine Learning,The Bayesian Approach to Machine Learning, A Revealing Introduction to Hidden Markov Models, Introduction to Reinforcement Learning, Deep Learning for Feature Representation, Neural Networks and Deep Learning, AI-Completeness: The Problem Domain of Super-intelligent Machines.

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s 178Pages

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.

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s 359Pages

Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra

This book covers the following topics: AI Technique, Level of the Model,Problem Spaces, and Search: Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques: Generate-andTest, Hill Climbing, Best-first Search, Problem Reduction, Constraint Satisfaction, Means-ends, Symbolic Reasoning Under Uncertainty, Game Playing, Learning: Rote Learning.

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s 128Pages

Machine Learning Advanced Techniques and Emerging Applications

This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

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s NAPages

Introduction to Artificial Intelligence by Bojana Dalbelo Basic and Jan Snajder

This note covers the following topics: Atlas: humanoid robot, VoiceTra Real-Time Machine Translation, DeepMind&

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s 79Pages

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.

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Artificial Intelligence by Prof. Pallab Dasgupta and Prof. Partha Pratim Chakrabarti

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.

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Introduction to Artificial Intelligence by Cristina Conati

This note provides an introduction to the field of artificial intelligence. Major topics covered includes: reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.

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s NAPages

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.

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Artificial Intelligence Lecture Materials

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.

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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.

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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.

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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.

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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.

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Artificial Intelligence Lecture Notes Veer Surendra Sai University

This lecture note covers the following topics: Formalized symbolic logic, Probabilistic Reasoning Structured knowledge, graphs, frames and related structures, Matching Techniques, Knowledge organizations, Management, Natural Language processing, Pattern recognition, expert systems.

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s 213Pages

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.

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Artificial Intelligence by IIT Kharagpur

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.

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Artificial Intelligence Course Notes

This note explains artificial intelligence, including agent design, heuristic search, knowledge representation, planning, logic, natural language processing and machine learning.

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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.

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Artificial Intelligence Lecture Notes

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.

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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.

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Building Expert Systems In Prolog (Amzi)

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

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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.

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Introduction to Machine Learning (N. Nilsson)

This note covers the following topics: Preliminaries, Boolean Functions, Using Version Spaces for Learning, Statistical Learning, Decision Trees, Inductive Logic Programming , Computational Learning Theory, Unsupervised Learning, Temporal-Difference Learning, Delayed-Reinforcement Learning.

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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.

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Practical Artificial Intelligence Programming in Java

This book is for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical Artificial Intelligence (AI) programming and information processing techniques. Topics covered includes: Search, Reasoning, Semantic Web, Expert Systems, Genetic Algorithms, Neural Networks, Machine Learning with Weka, Statistical Natural Language Processing.

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s 222Pages

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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s Pages

Building Expert Systems In Prolog

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

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s Pages

Introduction to Machine Learning

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

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s Pages

Artificial Intelligence I (W. Jones)

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

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s Pages

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