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Hey, I am new to the field of machine learning and recently started reading the book Machine Learning by Tom Mitchell and am stuck on a particular section in the first chapter where he talks about Estimating Training values and also adjusting the weights. An explanation of the concepts of estimating training values would be great but I understand that it is not easy to explain all this so I would be really obliged if someone would be able to point me towards a resource (lecture video, or simple lecture slides, or some text snippet) that talks about the concept of estimating training data and the like.

Again I am sorry I cannot provide more information in terms of the question I am asking. The book sections are 1.2.4.1 and 1.2.4.2 in "Machine Learning by Tom Mitchell" if anyone has read this book and has had the same problem in understanding the concepts described in these sections.

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Value in this context should be interpreted as a measure of the quality or performance of a certain state or instance, not as "values" as in numbers in general. Using his checker's example, a state with a high value is a board situation that is good/advantageous for the computer player.

The main idea here is that if you can provide every possible state that can be encountered with a value, and there is a set of rules that defines which states can be arrived from the current state by doing which actions, then you can make an informed decision about which action to take.

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