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.
Author(s): Seoul National University
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
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 Na and iuml,ve Bayes, Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian networks and Markov models.
Author(s): Thomas P Trappenberg, Dalhousie University
208 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
