## ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGPrimarily intended for the undergraduate and postgraduate students of computer science and engineering, this text bridges the gaps in knowledge of the seemingly difficult areas of artificial intelligence and machine learning. This book promises to provide the most number of case studies and worked out examples than any other of its genre. The text is written in a highly interactive manner which makes for an avid reading. More into the text, the contents are well placed that it takes off from the introduction to AI, which is followed by heuristics searching and game playing. The machine learning section begins with the basis of learning, and the various association rule learning algorithms. Various types of learning like, reinforced, supervised, unsupervised and statistical are also included with numerous case studies and application exercises. The well explained algorithms and pseudo codes for each topic make this book useful for students. KEY FEATURES • Includes Case studies for each machine learning algorithm • Incorporates day to day examples and pictorial representations for a deeper understanding of the subject • Helps students to create programs easily |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

3 | |

KNOWLEDGE REPRESENTATION 7081 | |

KNOWLEDGE REPRESENTATION STRUCTURES 82107 | |

REASONING 108124 | |

ASSOCIATION LEARNING 146166 8 1 Basics of Association 146 | |

9 | |

10 | |

STATISTICAL LEARNING 201249 11 1 Hidden Markov Models 202 11 1 1 Stochastic Processes 202 11 1 2 Markov Process 203 | |

ANN 264 12 7 2 Weight Balancing BackPropagation Algorithm 265 | |

SUPERVISED | |

EXPERT SYSTEMS 320339 | |

HEURISTIC | |

### Common terms and phrases

action agent applications Apriori algorithm artificial intelligence association rule attributes backward chaining Bayesian networks calculated canbe centroid classifier Consider database dataset decision problem decision tree defined depends distance domain environment evaluation example expert system firstorder FP tree frame frequent item sets function fuzzy set given goal graph heuristic hidden hidden Markov model Inductive Inductive logic programming inference initialised input inthe iterations kmeans knowledge base knowledge representation learning algorithm logic programming machine learning Markov model matrix method move natural language neural network neuron node objects observations ofthe onthe parameters path patterns performance positive prediction procedure random Random forest reasoning reinforcement learning represented resolution reward robot selected semantic semantic networks sentences sequence setof shown in Figure similar situation solution solve state3 step stochastic structure supervised learning symbols technique tothe transition probability unsupervised variables vector weights word