ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Primarily 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
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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
EXPERT SYSTEMS 320339
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