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.
Author(s): Prof. Pallab Dasgupta and Prof. Partha Pratim Chakrabarti
NA Pages 
Artificial Intelligence Foundations of Computational Agents
This book contains below topics, What is artificial intelligence, Agent architectures and search, Reasoning with constraints, Propositions and inference, Planning, Supervised machine learning, Neural networks and deep learning, Reasoning and planning under uncertainty, Multiagent systems, Reinforcement learning.
Author(s): David L. Poole and Alan K. Mackworth, University of British Columbia
904 Pages
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 FirstOrder Logic, knowledge engineering, resolution, classical planning, and planning with state space search.it also handles with topics like acting in nondeterministic domains and multiagent planning, Bayes Rule, Bayesian Networks and Dempster Shafer Theory.It includes learning decision trees and the role of knowledge in learning.
Author(s): Department of Information Technology , Malla Reddy College Of Engineering and Technology
143 Pages
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, AICompleteness, The Problem Domain of Superintelligent Machines.
Author(s): Prof. Roberto V. Zicari
178 Pages
Machine Learning Advanced Techniques and Emerging Applications
Hamed Farhadis online book Machine Learning, Advanced Techniques and Emerging Applications and quot, explores the latest developments in machine learning and their various applications It explores recent developments in machine learning techniques, and focuses on how these innovations are changing various industries. The book emphasizes the integration of these techniques into smart cities, where machine learning enhances urban management and infrastructure through datadriven solutions This includes the role of automation in, including how advanced algorithms simplify manufacturing, improve efficiency and enable customization In terms of providing details, the text explores the role of machine learning in emerging industries, and shows how startups and innovations can use these technologies to gain competitive advantage and drive innovation. Overall, the book provides a comprehensive overview of how advanced machine learning techniques are being used in various.
Author(s): Hamed Farhadi
NA Pages
