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the available training examples The LMS algorithm for fitting weights achieves this goal by iteratively tuning the weights, adding a correction to each weight each time the hypothesized evaluation function predicts a value that differs from the training value This algorithm works well when the hypothesis representation considered by the learner defines a continuously parameterized space of potential hypotheses Many of the chapters in this book present algorithms that search a hypothesis space defined by some underlying representation (eg, linear functions, logical descriptions, decision trees, artificial neural networks) These different hypothesis representations are appropriate for learning different kinds of target functions For each of these hypothesis representations, the corresponding learning algorithm takes advantage of a different underlying structure to organize the search through the hypothesis space Throughout this book we will return to this perspective of learning as a search problem in order to characterize learning methods by their search strategies and by the underlying structure of the search spaces they explore We will also find this viewpoint useful in formally analyzing the relationship between the size of the hypothesis space to be searched, the number of training examples available, and the confidence we can have that a hypothesis consistent with the training data will correctly generalize to unseen examples

One essential fact that simplifies attempts to derive confidence intervals is the Central Limit Theorem Consider again our general setting, in which we observe the values of n independently drawn random variables Yl Yn that obey the same unknown underlying probability distribution (eg, n tosses of the same coin) Let p denote the mean of the unknown distribution governing each of the Yi and let a denote the standard deviation We say that these variables Yi are independent, identically distributed random variables, because they describe independent experiments, each obeying the same underlying probability distribution In an attempt to estimate the mean p of the distribution governing the Yi, we calculate the sam= ' Yi (eg, the fraction of heads among the n coin tosses) ple mean & The Central Limit Theorem states that the probability distribution governing Fn approaches a Normal distribution as n + co,regardless of the distribution that governs the underlying random variables Yi Furthermore, the mean of the distribution governing Yn approaches p and the standard deviation approaches k More precisely,

Adams, Robert L, ed The Adams Cover Letter Holbrook, MA: Adams Publishing, 1995 Asher, Donald The Overnight Job Change Letter Berkeley, CA: Ten Speed Press, 1994 Beatty, Richard H 175 High Impact Cover Letters New York: John Wiley & Sons, Inc, 2002 Besson, Taunee Cover Letters New York: John Wiley & Sons, 1989 Burgett, Gordon The Writer s Guide to Query Letters and Cover Letters Rocklin, CA: Prima Publishing, Inc, 1991 Farr, Richard The Quick Resume and Cover Letter Book Indianapolis, IN: JIST Works, 1994 Frank, William S 200 Letters for Job Hunters Berkeley, CA: Ten Speed Press, 1993 Hansen, Katherine, and Randall Hansen Dynamic Cover Letters: How to Write the Letter That Gets You the Job Berkeley, CA: Ten Speed Press, 2001 Kaplan, Bonnie Miller Sure-Hire Cover Letters New York: American Management Association, 1994 Krannich, Ronald L, and Caryl Rae Krannich Dynamic Cover Letters and Other Great Job Search Letters Manassas Park, VA: Impact Publications, 1998 The Perfect Cover Letter New York: John Wiley & Sons, 1997 Krannich, Ronald L, and William J Banis High Impact Resumes and Letters 6th ed Manassas, VA: Impact Publications, 2002 Marler, Patty, and Jan Bailey Mattia Cover Letters Made Easy Lincolnwood, IL: VGM Career Horizons, 1996 Martin, Eric R, and Karyn E Langhorne How to Write Successful Cover Letters Lincolnwood, IL: VGM Horizons, 1994 Neal, James E, and Dorothy J Neal Effective Letters for Business, Professional and Personal Use Perrysburg, OH: Neal Publications, Inc, 1999 Provenzano, Steven Top Secret Resumes and Cover Letters Dearborn, MI: Financial Publishing, Inc, 1996 Wynett, Stanley Cover Letters That Will Get You the Job You Want Cincinnati: Better Way Books, 1993

Let s get to it. Go to the developer site, register as an iPhone Developer if you haven t already done so (http://developer.apple.com/iphone/program/start/register/), and then go to the downloads page to get the iPhone SDK (http://developer.apple.com/technology/xcode.html). By the time you read this, Apple may have released a combined Xcode installation for both Mac OS X and iPhone, in which case you will need to download and install this. It will overwrite your Mac OS X Xcode installation but should not affect your preferences. Once you ve downloaded the kit, you install the iPhone Xcode tools in the usual way. The main difference that you will see once the iPhone SDK is in place is that you will have some new options in the Xcode New Project window (see Figure 4 1). As well as the Mac OS X options, you now have an entry in the list for the iPhone OS.

Theorem 51 Central Limit Theorem Consider a set of independent, identically distributed random variables Yl Y, governed by an arbitrary probability distribution with mean p and finite variance a2Define the sample mean, = Yi

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