, English, Book, Illustrated edition: Learning from data: a short course / Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin. LEARNING FROM DATA. The book website AMLbook. com contains supporting material for instructors and readers. LEARNING FROM DATA A SHORT. Caltech Online Course Slides. Slides directory for the 18 lectures of the Learning From Data telecourse: Slides of 'Learning From Data' MOOC. (created by.
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Dynamic e-Chapters. As a free service to our readers, we are introducing e- Chapters that cover new topics that are not covered in the book. These chapters are. Does anybody have any experience with the Learning from Data textbook by Yaser S. I can't comment on that book, but since it's always good to have options, to this (with free PDF download): medical-site.info~ullman/ medical-site.info This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the.
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The book focuses on the mathematical theory of learning, why it's feasible, how well one can learn in theory, etc. Pretty hardcore math, Well-written and carefully presented book.
FYI, Dr. Abu-Mostafa has a class based on this book, which is available on Youtube. Thanks https: Please enable cookies in your browser to get the full Trove experience. Skip to content Skip to search.
Home This edition , English, Book, Illustrated edition: Learning from data: Abu-Mostafa, Yaser S. Published [United States]: Language English. Check copyright status Cite this Title Learning from data: Author Abu-Mostafa, Yaser S.
Lin, Hsuan-Tien. Physical Description xii, p. Subjects Machine learning -- Textbooks. Contents 1. Distributors , book stores and Instructors: Its techniques are widely applied in engineering, science, finance, and commerce.
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. 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. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. In contrast to supervised learning where the training examples were of the form input , correct output , the examples in reinforcement learning are of the form input , some output , grade for this output.
Importantly, the example does not say how good other outputs would have been for this particular input. Reinforcement learning is especially useful for learning how to play a game.
Imagine a situation in backgammon where you have a choice between different actions and you want to identify the best action. It is not a trivial task to ascertain what the best action is at a given stage of the game, so we cannot 12 1.
They still f all into clusters. The rule may be somewhat ambiguous, as type 1 and type 2 could be viewed as one cluster easily create supervised learning examples.
If you use reinforcement learning instead, all you need to do is to take some action and report how well things went, and you have a training example.
The reinforcement learning algorithm is left with the task of sorting out the information coming from different ex amples to find the best line of play. We are just given input examples xi, , XN. You may wonder how we could possibly learn anything from mere inputs. Consider the coin classification problem that we discussed earlier in Figure 1. Suppose that we didn't know the denomination of any of the coins in the data set.
This unlabeled data is shown in Figure l. We still get similar clusters , but they are now unlabeled so all points have the same 'color'. The decision regions in unsupervised learning may be identical to those in supervised learning, but without the labels Figure 1. However, the correct clustering is less obvious now, and even the number of clusters may be ambiguous.
Nonetheless, this example shows that we can learn something from the inputs by themselves. Unsupervised learning can be viewed as the task of spontaneously finding patterns and structure in input data.
For instance, if our task is to categorize a set of books into topics, and we only use general properties of the various books, we can identify books that have similar prop erties and put them together in one category, without naming that category.
Imagine that you don't speak a word of Spanish, but your company will relocate you to Spain next month. They will arrange for Spanish lessons once you are there, but you would like to prepare yourself a bit before you go. All you have access to is a Spanish radio station. For a full month, you continuously bombard yourself with Spanish; this is an unsupervised learning experience since you don't know the meaning of the words.
However, you gradually develop a better representation of the language in your brain by becoming more tuned to its common sounds and structures. When you arrive in Spain, you will be in a better position to start your Spanish lessons.
Indeed, unsupervised learning can be a precursor to supervised learning. In other cases, it is a stand-alone technique.
I f a task can fit more tha n one type, explain how a nd describe the tra i n i n g data for each type. As a result, learning from data is a diverse subject with many aliases in the scientific literature. The main field dedicated to the subject is called machine learning, a name that distinguishes it from human learning. We briefly mention two other important fields that approach learning from data in their own ways.
Statistics shares the basic premise of learning from data, namely the use of a set of observations to uncover an underlying process. In this case, the process is a probability distribution and the observations are samples from that distribution. Because statistics is a mathematical field, emphasis is given to situations where most of the questions can be answered with rigorous proofs. As a result, statistics focuses on somewhat idealized models and analyzes them in great detail.
This is the main difference between the statistical approach 14 1. The first two rows show the training examples each input x is a 9 bit vector represented visually as a 3 x 3 black and white array. Your task is to learn from this data set what f is, then apply f to the test input at the bottom. We make less restrictive assumptions and deal with more general models than in statistics. Therefore, we end up with weaker results that are nonetheless broadly applicable. Data mining is a practical field that focuses on finding patterns, correla tions, or anomalies in large relational databases.
For example, we could be looking at medical records of patients and trying to detect a cause-effect re lationship between a particular drug and long-term effects.
We could also be looking at credit card spending patterns and trying to detect potential fraud.
Technically, data mining is the same as learning from data, with more empha sis on data analysis than on prediction. Because databases are usually huge, computational issues are often critical in data mining. Recommender systems, which were illustrated in Section 1. The target function f is the object of learning. The most important assertion about the target function is that it is unknown.
We really mean unknown. This raises a natural question. How could a limited data set reveal enough information to pin down the entire target function? A simple learning task with 6 training examples of a 1 target function is shown.
Try to learn what the function is then apply it to the test input given. Now, show the problem to your friends and see if they get the same answer. The chances are the answers were not unanimous, and for good reason. Both functions agree with all the examples in the data set, so there isn't enough information to tell us which would be the correct answer.