Artificial Intelligence by Prof. Hugh Murrell

Lecture note on Artificial Intelligence

St. Ann Engineering and Technology 'Lecture Notes on Artificial Intelligence' provides a comprehensive introduction to basic AI concepts. It begins with an introduction to AI and product design, lays the foundation for understanding the fundamentals and fundamental structure of artificial intelligence and then the presentation delves into the knowledge base, drawing the focus is on how information is structured and used in AI systems. It explores the Definition of Knowledge through Predicate Logic, and explains how formal logic is used to represent complex information and relationships. The section on knowledge measurement describes methods for extracting new information from existing knowledge about the AI system. The presentation is about systems and machine learning, about strategic decision-making methods and adaptive learning in AI. Finally, it discusses expert systems and metaknowledge, explores advanced systems designed to mimic human knowledge, and examines the role of higher-order knowledge in AI applications. This resource provides a comprehensive overview of important AI topics spanning both theoretical and practical aspects of the field.

Author(s): St Anne College of Engineering and Technology

173 Pages

Introduction to Artificial Intelligence by Thomas P Trappenberg

Introduction to Artificial Intelligence by Thomas P. Trappenberg provides a comprehensive insight into AI concepts, presented by Dalhousie University. The essay begins with an introduction and history, providing a foundation for the development of AI , It includes research designs and their applications, followed by Robotics and Motion Planning, which are robotic While exploring the integration of AI in the design, the paper delves into constraint satisfaction problems, dealing with solution methods handles complex constraints, including learning machines and perceptrons, and improves with regression, classification , and maximum likelihood techniques support vector machines, learning theory, and naive Bayes and other generative models, probabilistic reasoning is also available, including Bayesian networks and Markov models Overview of essential AI methods and applications.

Author(s): Thomas P Trappenberg, Dalhousie University

208 Pages

Digital notes on Artificial Intelligence

Department of Information Technology's 'Digital Notes on Artificial Intelligence' at Malla Reddy College of Engineering and Technology provides a comprehensive overview of AI concepts. It begins with an introduction to AI, setting up more advanced topics . The essays include a variety of search methods, including A Search, and overcome challenges such as partial discovery searches. Techniques such as Alpha-Beta Pruning have been introduced in order to optimize the search process. The text explores ways of understanding including forward and backward chains, and delves into the syntax and semantics of first-order meaning. This includes all knowledge technologies, decision-making, and classical planning including state space exploration. In addition, it involves practice in random environments, multidimensional design, probabilistic reasoning using Bayes rules, Bayesian networks, Dempster-Shafer theorem The presentation goes on to say useful things on such as learning decision trees and the importance of knowledge in curriculum design.

Author(s): Department of Information Technology , Malla Reddy College Of Engineering and Technology

143 Pages

Explorations in Artificial Intelligence and Machine Learning

Professor Roberto V. Jikari's PDF titled 'Exploring Artificial Intelligence and Machine Learning' provides a comprehensive overview of key concepts in AI and ML It begins with an introduction to machine learning, with algorithms and methods that it begins to include. The paper then examines The Bayesian Approach to Machine Learning, emphasizing theoretical possibilities and statistical methods. It provides a comprehensive review of Hidden Markov Models, and explains their use in sequential forecasting. The introduction to reinforcement learning is about how employees learn optimal behavior through interaction with their environment. Deep Learning for Feature Representation discusses advanced techniques for extracting meaningful features from data using deep networks. The section on Neural Networks and Deep Learning explores neural network design and training in detail. Finally, the text discusses AI in general, focusing on the challenges and implications of building highly intelligent machines.

Author(s): Prof. Roberto V. Zicari

178 Pages