Machine Learning: An Algorithmic Perspective (Hardcover)

Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.

Theory Backed up by Practical Examples

The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.

Highlights a Range of Disciplines and Applications

Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

傳統的機器學習書籍可以分為兩類 - 一類針對具備合理數學知識的高年級本科生或初級研究生，另一類則是介紹如何編寫算法的入門書籍。現在正是需要一本書，不僅展示了構成機器學習方法的算法的使用方法，還提供了理解這些算法如何以及為什麼工作所需的背景知識。《機器學習：算法視角》就是這樣一本書。

這本書涵蓋了神經網絡、圖模型、強化學習、演化算法、降維方法以及重要的優化領域。它在適度的學術嚴謹性和避免用方程式和數學概念淹沒學生之間取得了平衡。作者以實用的方式討論了這些主題，同時提供了完整的信息和參考資料，以便查找其他解釋。他使用了基於廣泛可用數據集的例子，並提供了測試對材料的理解和應用的實際和理論問題。該書通過代碼示例描述了算法，並提供了一個網站，其中提供了Python的工作實現。作者使用了各種應用的數據來演示這些方法，並為學生提供了實際問題來解決。

這本書突出了機器學習的各個學科和應用領域。機器學習的多學科性質從計算機科學、統計學、數學和工程學中獲得了強調，並且可以應用於從金融到生物醫學、物理學到化學的各個領域。這本書以易於理解的風格撰寫，彌補了學科之間的差距，提供了理論和實際應用知識的理想結合。