Machine Learning: The New AI (The MIT Press Essential Knowledge series)

Ethem Alpaydin

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商品描述

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.

商品描述(中文翻譯)

今天,機器學習已經成為我們每天使用的各種應用的基礎,從產品推薦到語音識別,以及一些我們尚未每天使用的應用,包括無人駕駛汽車。它是計算中的一種新方法的基礎,我們不再編寫程序,而是收集數據;這種方法的想法是從數據中自動學習任務的算法。隨著計算設備變得更加普及,我們生活和工作的更大部分被數字記錄下來,隨著“大數據”的增長,機器學習理論(將數據處理為知識的基礎)也得到了發展。在這本書中,機器學習專家Ethem Alpaydin為一般讀者提供了一個簡明的主題概述,描述了其演變,解釋了重要的學習算法,並提供了示例應用。

Alpaydin描述了數字技術如何從數據處理的大型主機發展到移動設備,將今天的機器學習繁榮置於背景中。他介紹了機器學習的基礎知識和一些應用;使用機器學習算法進行模式識別;受人腦啟發的人工神經網絡;學習實例之間關聯的算法,應用包括客戶分割和學習推薦;以及強化學習,當自主代理學習如何最大化獎勵和最小化懲罰。然後,Alpaydin考慮了機器學習的一些未來方向和“數據科學”的新領域,並討論了數據隱私和安全性的道德和法律影響。