An Introduction to Machine Learning 2/e

Miroslav Kubat

買這商品的人也買了...

商品描述

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.

 

This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

商品描述(中文翻譯)

這本教科書以易於理解的方式介紹了基本的機器學習概念,提供實用的建議、使用直觀的例子,並提供有趣的相關應用討論。主要主題包括貝葉斯分類器、最近鄰分類器、線性和多項式分類器、決策樹、神經網絡和支持向量機。後面的章節展示了如何通過“增強”來結合這些簡單工具,如何在更複雜的領域中利用它們,以及如何處理各種高級實際問題。其中一章專門介紹了流行的遺傳算法。

這個修訂版包含了三個全新的章節,涉及機器學習在工業中實際應用的關鍵主題。這些章節探討了多標籤領域、無監督學習及其在深度學習中的應用,以及歸納的邏輯方法。許多章節得到了擴展,並且材料的呈現方式也得到了增強。本書包含了許多新的習題、大量解答的例子、引人思考的實驗,以及獨立工作的計算機任務。