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Artificial Intelligence Lecture Notes

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Artificial Intelligence Lecture Notes

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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Lecture note on Artificial Intelligence

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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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.

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Digital notes on Artificial Intelligence

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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Introduction to Artificial Intelligence by Bojana Dalbelo Basic and Jan Snajder

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Techniques of Artificial Intelligence by Vrije Universiteit Brussel

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

Artificial Intelligence Techniques Notes

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Techniques in Artificial Intelligence

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

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 Lecture Notes

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.

sNA Pages
Machine Learning, Neural and Statistical Classification

Machine Learning, Neural and Statistical Classification

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