A First Course in Machine Learning (Hardcover)

A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail.

Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB^®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems.

Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.

《機器學習初級課程》涵蓋了理解一些最受歡迎的機器學習算法所需的核心數學和統計技巧。所介紹的算法涵蓋了機器學習的主要問題領域：分類、聚類和投影。本書詳細描述和推導了少數算法，而不是輕描淡寫地涵蓋許多算法。

本書中的文本引用了一個支援網站（http://bit.ly/firstcourseml），該網站提供了大量的MATLAB®/Octave腳本，使學生能夠重現書中的圖表，並研究模型規格和參數值的變化。通過實驗各種算法和概念，學生可以看到如何使用抽象的方程組來解決實際問題。

本書的教材經過課堂測試，對數學要求很少，提供了簡潔易懂的機器學習入門。它為學生提供了探索機器學習文獻並深入研究特定方法的知識和信心。