Learning From Data (Hardcover)

Yaser S. Abu-Mostafa , Malik Magdon-Ismail , Hsuan-Tien Lin

  • 出版商: AMLBook
  • 出版日期: 2012-03-26
  • 定價: $1,200
  • 售價: 9.5$1,140
  • 語言: 英文
  • 頁數: 213
  • 裝訂: Hardcover
  • ISBN: 1600490069
  • ISBN-13: 9781600490064
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<內容簡介>

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. 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. 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. 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 performance of real learning systems.
Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own.
The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

<章節目錄>

Ch1: The Learning Problem
Ch2: Training versus Testing
Ch3: The Linear Model
Ch4: Overfitting
Ch5: Three Learning Principles

商品描述(中文翻譯)

內容簡介:

機器學習允許計算系統根據觀察到的數據不斷改進其性能。它的技術被廣泛應用於工程、科學、金融和商業領域。本書旨在提供一個關於機器學習的短期課程。這是一個短期課程,而不是一個匆忙的課程。通過十多年的教學經驗,我們提煉出了我們認為每個學習者都應該知道的核心主題。我們選擇了「從數據中學習」這個標題,忠實地描述了這個主題的內容,並以故事的方式來介紹這些主題。我們希望讀者能夠通過閱讀全書來學習這個主題的所有基礎知識。

從數據中學習有明顯的理論和實踐兩個方向。在本書中,我們平衡了理論和實踐、數學和啟發式方法。我們的選擇標準是相關性。包括建立學習概念框架的理論,以及影響實際學習系統性能的啟發式方法。

從數據中學習是一個非常動態的領域。有些熱門的技術和理論有時只是一時的風尚,而其他一些則獲得了廣泛應用並成為該領域的一部分。在本書中,我們強調的是為從數據中學習的任何學習者打下堅實基礎所必需的基本原理,使他們能夠進一步探索更多的技術和理論,或者可能做出自己的貢獻。

作者是加州理工學院(Caltech)、倫斯勒理工學院(RPI)和國立臺灣大學(NTU)的教授,本書是他們在機器學習方面的熱門課程的主要教材。作者還在金融和商業公司廣泛咨詢機器學習應用,並帶領贏得機器學習競賽的團隊。

章節目錄:

第一章:學習問題
第二章:訓練與測試
第三章:線性模型
第四章:過度擬合
第五章:三個學習原則