Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra
Advertisement
Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra
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.
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
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
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.
This book covers recent advances of machine learning techniques in a
broad range of applications in smart cities, automated industry, and emerging
businesses.
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.
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
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.
Author(s): Prof. Tomas Lozano-Perez and Prof.
Leslie Kaelbling
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.