圖書標籤: 機器學習 統計學習 統計學 數據挖掘 數學 統計 數據分析 statistics
发表于2025-02-22
統計學習基礎(第2版)(英文) pdf epub mobi txt 電子書 下載 2025
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
書名翻譯有誤。應該譯為《統計學習精要》比較好,數學基礎不好的可以對照著《統計學習導論》學習,從事機器學習理論研究的應該要看看《統計學習理論》這本著作。總的來說,如果時間充裕的話,還是必須要高屋建瓴,看一些深刻的書籍的。隻有打好嚴謹紮實的基礎,纔能跟上機器學習領域的發展呐==
評分很適閤CS同學閱讀
評分Good Introduction with detailed explanation in spite of tediousness.
評分名氣很大,內容很散,不如直接讀論文
評分其實這本書有個姐妹篇,叫 An Introduction to Statistical Learning: with Applications in R ,是Hastie 和Tibshirani 和另外兩個作者閤寫的,更加適閤入門,是非常經典的教材。
The methodology used in the books are fancy and attractive, yet in terms of rigorous proofs, sometimes the book skip steps and is difficult to follow. ~ Slightly sophisticated for undergraduate students, but in general is a very nice book.
評分[https://esl.hohoweiya.xyz/index.html] ==========================================================================================================================================================
評分读了一个月,还在前四章深耕,在此说明一下,网上的 solution,笔记啊,我见到的,只有一个份做的最详细,准确度最高,其余的都是滥竽充数,过程推导乱来,想当然,因为该书的符号有点混乱,所以建议阅读该书的人把前面的 Notation 读清楚,比如书中 X 出现的有好几种形式,每...
評分统计学习的经典教材,数学难度适中,英文难度较低,看了其中有监督学习部分,无监督学习部分没怎么看,算法比较经典,但是也比较老。
評分非常难,一点都不element,是本百科全书式的读物,如果是初学者,不建议读 很多章节也没有细节,概述性的东西,能看懂几章就很不错了 其实每章都可以写成一本书,都可以做很多篇的论文 全部读懂非常非常难,倒是作为用到哪个部分作为参考资料查查很不错
統計學習基礎(第2版)(英文) pdf epub mobi txt 電子書 下載 2025