Machine Learning for Engineers (Hardcover)

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.

這本自成一體的機器學習入門書籍從一開始就針對工程師設計，旨在為學生提供一切開始應用機器學習原理和算法解決實際工程問題所需的知識。著重於估計、檢測、資訊理論和優化之間的關聯，內容包括：機器學習與信號處理之間關係的易於理解的概述，為進一步學習打下堅實基礎；清晰解釋最先進技術與傳統方法之間的差異，使學生完全理解並能做出明智的技術選擇；演示資訊理論概念與實際工程相關性之間的聯繫；使用Matlab的可重現示例，讓學生進行實際操作。本書假設讀者只具備基本的概率和線性代數知識，並附有講義和解答供教師使用，是各個工程學科的學生理想的機器學習入門教材。