The breadth of A. I. is explored and explained in this best selling text. Assuming no prior knowledge,it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.
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Read this expression as the probability of hypothesis H given that we have
observed evidence E. To compute this , we need to take into account the prior
probability of H ( the probability that we would assign to H if we had no evidence )
We can state this in a table in which the conditional probabilities are provided .
We show such a table for our example in Figure 8.3 . For example , from the table
we see that the prior probability of the rainy season is 0.5 . Then , if it is the rainy ...
We also require a mechanism for using the graph that guarantees that
probabilities are transmitted correctly . ... The message - passing approach is
based on the observation that to compute the probability of a node A given what
is known ...
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very poor book for novice.Assumes u already know AI and the language is not at all user friendly.One has to read a sentence repeatedly to get a grasp and some times even then u don't understand.Go for other books much better than this
not soo good to go for novice
What Is Artificial Intelligence?
Problems Problem Spaces and Search
Heuristic Search Techniques
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