Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
發表於2024-06-23
Learning From Data 2024 pdf epub mobi 電子書 下載
在CIT的機器學習和數據挖掘課程上看到這本書,目錄看起來很不錯,應該比Andrew Ng課程更偏重理論些。這本書就是CIT課程授課內容的總結,這種書看起來比直接看教材要容易多,隻是一直沒有找到這本書,請問有人有電子版嗎?
評分前後曆時半年多,總算把LFD的習題整理完瞭,除瞭第六章,第八章和第九章少部分習題以外,其他所有習題均已完成。教材的上半部分(第一章到第五章)是精髓,補充部分(第六章到第九章)有部分章節稍顯倉促,而且有一些小錯誤,第九章部分實際應用可能較少,但是總的來說,本書絕...
評分 評分 評分在CIT的機器學習和數據挖掘課程上看到這本書,目錄看起來很不錯,應該比Andrew Ng課程更偏重理論些。這本書就是CIT課程授課內容的總結,這種書看起來比直接看教材要容易多,隻是一直沒有找到這本書,請問有人有電子版嗎?
圖書標籤: 機器學習 MachineLearning 數據挖掘 數據分析 人工智能 計算機 DataMining 計算機科學
太……簡單……瞭。科普……吧?
評分林軒田蠻強的
評分太……簡單……瞭。科普……吧?
評分值得再讀一遍
評分這本書有公開課,在B站可以搜的到 關鍵字 “機器學習 加利福尼亞理工” 不過這門課網易也有帶中文字幕版本的,隻不過不是很全。這門課是我上過的最好的機器學習課程,原因是老師就是這本書的作者,講這些基礎的機器學習概念深入淺齣。而且這門課原本就是麵嚮網絡授課的,沒有瞭直接在課堂上錄像的那種公開課的蛋疼。相比於 NG 那門算法一籮筐的課,這門課著重點在於機器學習的靈魂,給你構造一個 soild 的知識體係,今後無論用到什麼算法,都可以用這一套方法去分析和設計。這是所有其他機器學習課程所不能做到的。後麵跟一本ESL或者PRML,統計機器學習可以解決瞭。
Learning From Data 2024 pdf epub mobi 電子書 下載