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Your first sight of Xcode, at least the first time you run it, is the Welcome to Xcode window. This window is a bit of an orphan: most articles and how-tos tend to dismiss it with a brief if you see the Welcome window, just uncheck the Show box and you ll never see it again . That s unfortunate, because in fact this window provides a wealth of useful information for the new Xcode developer. It s easy enough to dismiss or minimize the window, and the benefits of having it there as a signpost to useful resources far outweigh the hassle of that one click. By the way, if you have been experimenting with Xcode, opted not to show this window at startup, and then change your mind, you can reset Xcode defaults see It s All Gone Horribly Wrong below. One really good reason to keep the Welcome window as an Xcode startup item is that it now (as of Xcode v3.2) has a list of recent projects, giving you simple single-click access to your work. You are also one click away from a new project. The Getting started with Xcode link takes you to the Xcode Documentation Quick Start window, and this is a good entry point to tutorials, articles, and reference documentation. You will return to the documentation in 8, but it is worth making a visit to this window your first port of call when you need to understand something in Xcode. You are probably eager to get to Xcode itself, so let s leave the Welcome window there. My advice at least until you are really comfortable with Xcode s features, tools, and environment is to leave the Show at launch checkbox enabled. For now, though, minimize the window or close it.

In the case of noise-free training data, FOIL may continue adding new literals to the rule until it covers no negative examples To handle noisy data, the search is continued until some tradeoff occurs between rule accuracy, coverage, and complexity FOIL uses a minimum description length approach to halt the growth of rules, in which new literals are added only when their description length is shorter than the description length of the training data they explain The details of this strategy are given in Quinlan (1990) In addition, FOIL post-prunes each rule it learns, using the same rule post-pruning strategy used for decision trees ( 3)

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A second, quite different approach to inductive logic programming is based on the simple observation that induction is just the inverse of deduction! In general, machine learning involves building theories that explain the observed data Given some data D and some partial background knowledge B, learning can be described as generating a hypothesis h that, together with B, explains D Put more precisely, assume as usual that the training data D is a set of training examples, each of the form (xi, f (xi)) Here xi denotes the ith training instance and f (xi) denotes its target value Then learning is the problem of discovering a hypothesis h, such that the classification f (xi) of each training instance xi follows deductively from the hypothesis h, the description of xi, and any other background knowledge B known to the system

If you are accustomed to IDEs (Integrated Development Environments) like Eclipse or even REALbasic, you may be expecting a window crowded with panels, inspectors, and wizards. Having moved the Welcome screen out of the way, your first sight of the Xcode Workspace may be a little underwhelming. There is no immediately visible user interface nothing beyond the menu bar. This is part of the philosophy of Xcode development, and you will see it time and time again as we move through the book; you

(102) (V(xi, f (xi)) E D) (B Ah A xi) f (xi) The expression X F Y is read "Y follows deductively from X," or alternatively "X entails Y" Expression (102) describes the constraint that must be satisfied by the learned hypothesis h; namely, for every training instance xi, the target classification f (xi) must follow deductively from B, h, and xi As an example, consider the case where the target concept to be learned is "pairs of people (u, v) such that the child of u is v," represented by the predicate Child(u, v) Assume we are given a single positive example Child(Bob, Sharon), where the instance is described by the literals Male(Bob), Female(Sharon), and Father(Sharon, Bob) Furthermore, suppose we have the general background knowledge Parent (u, v) t Father (u, v) We can describe this situation in the terms of Equation (102) as follows: xi : Male(Bob), Female(Sharon), Father(Sharon, Bob)

f (xi) :

tend to see just what you need to see at any point in the development. For now, you are not actually doing anything, so there is no user interface to manipulate. In order to explore the Xcode Workspace, you are going to have to create a software project. You will learn a lot more over the coming chapters about what type of project to choose for the different applications you want to create, but for the purposes of exploring the environment you are just going to create and run a very simple application.

Child(Bob, Sharon)

In this case, two of the many hypotheses that satisfy the constraint (B Ah A xi) tf (xi) are hl : Child(u, v) t Father(v, u)