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Learning From Data blogger.com - Free Download
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. TLDR Summary: If Machine Learning is like Mechanics, "Learning from Data" teaches you Newton's Laws!Machine Learning (ML), Data Mining (DM), Predictive Modeling, Big Data, Statistical Inference, Pattern Recognition, Regression, Classification: by whichever name you call it, you will find hundreds of books by the same name, and in theoretical as well as applied avatars. The applied ones. Does anybody have any experience with the Learning from Data textbook by Yaser S. Abu-Mostafa from Caltech? I'm thinking of ordering it. I am working through the online lectures now, so I .
Learning from data yaser pdf download
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No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. The authors shall not be liable for any loss of learning from data yaser pdf download or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
The use in this publication of tradenames, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. This book was typeset by the authors and was printed and bound in the United States of America.
This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title 'learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion.
Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. Learning from data has distinct theoretical and practical tracks.
If you read two books that focus on one track or the other, you may feel that you are reading about two different subjects altogether.
In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the per formance of real learning systems.
Strengths and weaknesses of the different parts are spelled out. Our philosophy is to say it like it is: what we know, what we don't know, and what we partially know. The book can be taught in exactly the order it is presented.
The notable exception may be Chapter 2, which is the most theoretical chapter of the book. The theory of generalization that this chapter covers is central to learning from data, and we made an effort to make it accessible to a wide readership, learning from data yaser pdf download.
However, instructors who are more interested in the practical side may skim over it, or delay it until after the practical methods of Chapter 3 are taught. You will notice that we included exercises in gray boxes throughout the text. The main purpose of these exercises is to engage the reader and enhance understanding of a particular topic being covered.
Our reason for separating the exercises out is that they are not crucial learning from data yaser pdf download the logical flow. Nevertheless, they contain useful information, and we strongly encourage you to read them, even if you don't do them to completion. Instructors may find some of the exercises appropriate as 'easy' homework problems, and we also provide ad ditional problems of varying difficulty in the Problems section at the end of each chapter.
To help instructors with preparing their lectures based on the book, we provide supporting material on the learning from data yaser pdf download website AMLbook. There is also a forum that covers additional topics in learning from data. We will. Acknowledgment in alphabetical order for each group : We would like to express our gratitude to the alumni of our Learning Systems Group at Caltech who gave us detailed expert feedback: Zehra Cataltepe, Ling Li, Amrit Pratap, and Joseph Sill.
We thank the many students and colleagues who gave us useful feedback during the development of this book, especially Chun-Wei Liu.
The Caltech Library staff, especially Kristin Buxton and David McCaslin, have given us excellent advice and help in our self-publishing effort. We also thank Lucinda Acosta for her help throughout the writing of this book. Last, but not least, learning from data yaser pdf download, we would like to thank our families for their encourage ment, their support, and most of all their patience as they endured the time demands that writing a book has imposed on us.
Yaser S. Abu-Mostafa, Pasadena, California. Hsuan-Tien Lin, Taipei, Taiwan. Prefa e vii 1 The Learning Problem 1 1. Notation Index A complete table of learning from data yaser pdf download notation used in this book is included on pageright before the index of terms.
We suggest referring to it as needed. If you show a picture to a three-year-old and ask if there is a tree in it, you will likely get the correct answer. If you ask a thirty-year-old what the definition of a tree is, you will likely get an inconclusive answer. We didn't learn what a tree is by studying the mathematical definition of trees. We learned it by looking at trees.
In other words, we learned from 'data'. Learning from data is used in situations where we don't have an analytic solution, but we do have data that we can use to construct an empirical solu tion. This premise covers a lot of territory, and indeed learning from data is one of the most widely used techniques in science, engineering, and economics, among other fields.
In this chapter, learning from data yaser pdf download, we present examples of learning from data and formalize the learning problem. We also discuss the main concepts associated with learning, and the different paradigms of learning that have been developed.
What do financial forecasting, medical diagnosis, computer vision, and search engines have in common? They all have successfully utilized learning from data. The repertoire of such applications is quite impressive. Let us open the discussion with a real-life application to see how learning from data works. Consider the learning from data yaser pdf download of predicting how a movie viewer would rate the various movies out there.
This is an important problem if you are a company that rents out movies, since you want to recommend to different viewers the movies they will like, learning from data yaser pdf download. Good recommender systems are so important to business that the movie rental company Netflix offered a prize of one learning from data yaser pdf download dollars to anyone who could improve their recommendations by a mere The main difficulty in this problem is that the criteria that viewers use to rate movies are quite complex.
Trying to model those explicitly is no easy task, so it may not be possible to come up with an analytic solution. However, we. There is a great deal of data available to movie rental companies, since they often ask their viewers to rate the movies that they have already seen. Figure 1. Here is how it works, learning from data yaser pdf download. You describe a movie as a long array of different factors, e.
Now, you describe each viewer with corresponding factors; how much do they like comedy, do they prefer simple or complicated plots, how important are the looks of the lead actor, and so on. For example, if the movie is pure comedy and the viewer hates comedies, the chances are he won't like it.
If you take dozens of these factors describing many facets of a movie's content and a viewer's taste, the conclusion based on matching all the factors will be a good predictor of how the viewer will rate the movie. The power of learning from data is that this entire process can be auto mated, without any need for analyzing movie content or viewer taste.
To do so, the learning algorithm 'reverse-engineers' these factors based solely on pre. It starts with random factors, then tunes these factors to make them more and more aligned with how viewers have rated movies before, until they are ultimately able to predict how viewers rate movies in general. The factors we end up with may not be as intuitive as 'comedy content', and in fact can be quite subtle or even incomprehensible.
After all, the algorithm is only trying to find the best way to predict how a viewer would rate a movie, not necessarily explain to us how it is learning from data yaser pdf download. This algorithm was part of the winning solution in the million-dollar competition.
In order to abstract the common core of the learning problem, we will pick one application and use it as a metaphor for the different components of the problem. Let us take credit approval as our metaphor. Suppose that a bank receives thousands of credit card applications every day, and it wants to automate the process of learning from data yaser pdf download them.
Just as in the case of movie ratings, the bank knows of no magical formula that can pinpoint when credit should be approved, but it has a lot of data. This calls for learning from data, so the bank uses historical records of previous customers to figure out a good formula for credit approval.
Each customer record has personal information related to creditsuch as annual salary, years in residence, outstanding loans, etc. The record also keeps track of whether approving credit for that customer was a good idea, i.
This data guides the construction of a successful formula for credit approval that can be used on future applicants. Let us give names and symbols to the main components of this learning problem.
The examples are often referred to as data points, learning from data yaser pdf download. Finally, there is the learning algorithm learning from data yaser pdf download uses the data set D to pick a formula g: X -- Y that approximates f.
The algorithm chooses g from a set of candidate formulas under consideration, which we call the hypothesis set 1-l. For instance, 1-l could be the set of all linear formulas from which the algorithm would choose the best linear fit to the data, as we will introduce later in this section. When a new customer applies for credit, the bank will base its decision on g the hypothesis that the learning algorithm producednot on f the ideal target function which remains unknown.
The decision will be good only to the extent that g faithfully replicates f. To achieve thatthe algorithm. Whether or not this hope is justified remains to be seen. Exercise 1. We will use the setup in Figure 1. Later on, we will consider a number of refinements and variations to this basic setup as needed. However, the essence of the problem will remain the same. There is a target to be learned.
Lecture 01 - The Learning Problem
, time: 1:21:28Learning from data yaser pdf download
Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin-Learning From Data_ A short blogger.com ().pdf - Free ebook download as PDF File .pdf 5/5(5). Simple Way to Read Online Learning From Data: A Short Course by Yaser S. Abu-Mostafa Book or Download in PDF and Epub hi, my fellowship readers. A commonly searched for term is where to read book Learning From Data: A Short Course by Yaser S. Abu-Mostafa online. Learning From Data blogger.com - Free download Ebook, Handbook, Textbook, User Guide PDF files on the internet quickly and easily.
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