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Artificial Intelligence(1st Edition)

Authors:

R Panneerselvam

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Cover Type:Hardcover

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ISBN: B0BPJVXGC5

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Book Summary: R. Panneerselvam, Professor (Retd.), Pondicherry University With His 41 Years Of Teaching And Research Experience At Anna University, Chennai, And Pondicherry University, Brought Out This Comprehensive Text On Artificial Intelligence After Publishing The Books, Viz. Design And Analysis Of Algorithms, Databases And Python Programming, Problem Solving And Python Programming, R Programming Made Simple, Etc. This Text Is Designed For B.E./ B.Tech/ B.S Computer Science, M.E./ M.Tech/ M.S Computer Science, M.C.A And Data Science Degree Courses. The Text Begins With Basics Of Artificial Intelligence, Types Of AI, Working Of AI, Foundation Of AI, And AI Problems. Chapter 2 Presents Intelligent Agents, Which Include Categories Of Agents, Problem Formulation, Type Of Agents, Vacuum Cleaner Agent, PEAS Presentation Of Agent And Architecture Of Intelligent Agent. The Next Chapter On Expert Systems, Which Presents Knowledge-based Agent Architecture And Logic. Chapter 4 Presents Uninformed Search Algorithms Such As Breadth First Search And Depth First Search , Informed Search Algorithms Such As Greedy Search, A* Algorithm, Graph Search Algorithm Such As Graph Algorithm Applied To Shortest Path Problem And A* Algorithm Applied To 8 Puzzle Algorithm, Search Algorithm For Game Namely Tower Of Hanoi Problem, Constraint Satisfaction Problem, Means And End Analysis, And Heuristics. Chapter 5 Presents Metaheuristics, Viz. Simulated Annealing Algorithm, Genetic Algorithm, Ant Colony Optimization Algorithm, And Tabu Search. It Is Followed By A Chapter On Artificial Neural Network, Which Includes Biological Neuron Structure And Functions, Architecture Of Artificial Neural Network, Artificial Neurons Such As Sigmoid Function And Step Activation Function, Neural Network Topology, And Feedback Neural Network, Learning In Artificial Neural Network, Terminologies Of Neural Network, McCulloch-Pitts Models Of Neuron, Learning Rules In Neural Network, ADALINE, MADALINE And Backpropagation Algorithm With Workings. Chapter 7 Discusses Machine Learning On Topics Such As Classification Of Machine Learning, Data Mining, Big Data Analytics, Supervised Learning: Naïve Base Classifier With Its Steps, Algorithms, And Techniques Of Supervised Learning. Chapter 8 Presents KNN Algorithm, Decision Tree Classifier, And Train And Test Data Using Python Matplotlib. Chapter 9 On Multivariate Analysis Includes Correlation Analysis, Linear Regression Analysis Such As Simple Regression, Multiple Regression, Nonlinear Regression Analysis, And Logistic Regression With Workings. Chapter 10 Presents Model Selection Dilemmas In Clustering, Which Includes Concept Of Clustering, Hierarchical Clustering Algorithms, And Expectation Maximization Algorithm And Its Application To Coins Tossing. Chapter 11 On Kernel Machines Presents Kernel Method In Support Vector Machines, Hyper Plane, Working Of Support Vector Machine, Dot Products Of Vectors, Use Of Kernels In Support Vector Machines, Types Of Kernel Function And Working Of Kernel Method In SVM. Next Chapter 12 Presents Ensemble Methods, Which Include Gaussian Mixture Model, Expectation And Maximization Algorithms For Univariate Problem, And Multivariate Problem, Bagging And Stacking. The Next Chapter Presents Its Concept, Classification Of Boosting Algorithms, And SMO Algorithm. Chapter 14 Is On Advanced Multivariate Analysis, Presents Filtering Techniques, Wrapper Methods, Embedded Methods, Such As Regularization Methods, Random Forest Importance And Ridge Regression. Also, It Includes Methods On Feature Extraction, Discriminant Analysis, Factor Analysis, And Multidimensional Scaling. At The End Of This Text, Chapter15 On Robotics Is Included, Which Presents Different Coordinate Systems, Sensing, Precision Of Robot, Robotic Programming, Machine Learning Applications In Robotics, And Introduction To Robotic Languages.R. PANNEERSELVAM, B.E., M.E., Ph.D