统计学习基础
The Elements of Statistical Learning
Trevor Hastie、Robert Tibshirani、Jerome Friedman · 初版 2001 年
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为什么值得读
- 机器学习领域的开山经典教材与权威指南
- 深入从统计学视角剖析算法背后的数理原理
- 官方提供免费电子版便于系统性研读
阐述了 machine learning、data mining 和 bioinformatics 中的重要统计思想。涵盖范围广泛，从 supervised learning（prediction）到 unsupervised learning，包括 classification trees、neural networks 以及 support vector machines。
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……现在有点过时了，但它依然是该领域最平易近人、写得最好的书之一。另一本经得起时间考验的老书是 E.T. 杰恩斯的《概率论沉思录》。蒂布希拉尼的《统计学习要素》也很棒。安德鲁·格尔曼的《贝叶斯数据分析》是另一本神作。伊恩·古德费洛和约书亚·本吉奥的《深度学习》对于跟上最新进展很有帮助……
…tooth now, but it is still one of the most approachable and well written books in the field. Another old book that stands up very well is "Probability Theory: the Logic of Science" by E. T. Jaynes. "Elements of Statistical Learning" by Tibshirani is also good. "Bayesian Data Analysis" by Andrew Gelman is another great read. "Deep Learning" by Ian Goodfellow and Yoshua Bengio is useful for getting caught up with recent advances…
对于那些想更扎实地理解机器学习、而且还没把数学和概率论/统计学忘光的人（比如高年级本科生或刚入学的研究生），最好的教材似乎是：Hastie、Tibshirani 和 Friedman 合著的《统计学习要素》（Elements of Statistical Learning），网上有免费版。Chris Bishop 的《模式识别与机器学习》（Pattern Recognition and Machine Learning），非常贝叶斯。Kevin Murphy 的《机器学习：概率视角》（Machine Learning: A Probabilistic Perspective）……
For those who want a more solid take on machine learning, and who still remember their math and probability/statistics, (i.e. advanced undergrad or new grad student), the best texts seem to be: The Elements of Statistical Learning by Hastie, Tibshirani and Friedman, available for free on line. Pattern Recognition and Machine Learning by Chris Bishop. Very Bayesian. Machine Learning: A Probabilistic Perspective by Kevin Murphy.…
……成为一名数据科学家。有些课程则没啥用。Coursera 上的课程很适合带你入门；它们相当于大学入门级课程，如果你能啃完那些论文和经典教材（比如 Hastie 等人的《统计学习要素》），大概一两年就能进入中级水平。这并不容易，但绝对行得通。我认为要达到专家级别，就必须靠实战经验了……
…to becoming a data scientist. Some of it isn't. The Coursera courses are great for getting you started; they're entry-level college courses, and if you read the papers and the seminal textbooks (e.g. Elements of Statistical Learning by Hastie et al) you can get into the intermediate territory in a couple years or so. It's not easy, but it can definitely be done. Getting to the expert level, I think, just requires real-world experience…
* 统计学习要素 - Hastie, Tibshisrani * （要列的机器学习书可太多了：PRML、All of Stats、Deep Learning 等等） * 主动投资组合管理 - Kahn, Grinold * 思考，快与慢 - Kahneman * 蛋白质…
* Elements of Statistical Learning - Hastie, Tibshisrani * (Lot's of machine learning books to list: PRML, All of Stats, Deep Learning, etc.) * Active Portfolio Management - Kahn, Grinold * Thinking, fast and slow - Kahneman * Protein…
依我之见（数学硕士、应用统计学博士、目前在流行病学系做博后），基础就是像《统计学习要素》（https://hastie.su.domains/Papers/ESLII.pdf）这样的书。我觉得“数据科学”这个词根本毫无意义。机器学习就是一种通过统计来“做 AI”的方法。具体来说，它是统计学的一个分支……
In my opinion (MSc math, PhD in applied statistics, currently postdoc at epidemiology department), the fundamentals are books like The Elements of Statistical Learning: https://hastie.su.domains/Papers/ESLII.pdf I feel like the term data science is completely useless. Machine learning is an approach to "do AI" through statistics. Specifically, it is a branch of statistics…
……能给出更好的建议。但是，有两本书我真的觉得不容错过。统计学方面：David Freedman、Robert Pisani、Roger Purves 合著的《统计学（第三版）》。机器学习方面：Trevor Hastie、Robert Tibshirani 和 Jerome Friedman 合著的《统计学习要素》。
…with better advice. But, there are two books I really feel should not be missed. For statistics: "Statistics, Third Edition" David Freedman, Robert Pisani, Roger Purves. For machine learning: "The Elements of Statistical Learning" Trevor Hastie, Robert Tibshirani and Jerome Friedman.