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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.
Author(s): Dr. Prashanta Kumar Patra
128 Pages 
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.
Author(s): Department of CSE, Sri Indu College of Engineering and Technology
140 Pages
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.
Author(s): St Anne College of Engineering and Technology
173 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 Naïve Bayes, Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian networks and Markov models.
Author(s): Thomas P Trappenberg, Dalhousie University
208 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, AI-Completeness: The Problem Domain of Super-intelligent Machines.
Author(s): Prof. Roberto V. Zicari
178 Pages
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.
Author(s): Hamed Farhadi
NA Pages
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 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.
Author(s): Professor Yun Peng
NA Pages
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.
Author(s): Prof. Pradipta Kumar Das, Prof. D. Chandrasekhar Rao and Prof. Kishore Kumar Sahu
213 Pages
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.
Author(s): D. Michie, D.J. Spiegelhalter, C.C. Taylor
NA Pages
Machine Learning, Neural and Statistical Classification
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