…这些书里我推荐 Bishop 的《Pattern Recognition and Machine Learning》。我通读过这本书，结构排布和逻辑推导都非常出色。如果是更偏重数学深度的 ML 内容，我推荐 Mohri 的《Foundations of Machine Learning》。至于其它各方面的参考书，我推荐 Murphy 的《Machine Learning: A Probabilistic Perspective》。如果想深入了解图模型，可以去看看《Probabilistic Graphical Models: Principles…
…those I'd suggest "Pattern Recognition and Machine Learning" by Bishop. I've read throughout this and it's really well organized and thought out. For more mathematically advanced ML stuff I'd suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles…
…(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693...) Statistical/Theoretical: Shai Shalev-Schwartz & Shai Ben-David's Understanding Machine Learning (http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...) Mohri's Foundations of Machine Learning (https://cs.nyu.edu/~mohri/mlbook/) The two above courses could share SSS's Online Learning text (https://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf). To be fair, the stochastic variants of most optimization…
也可以考虑 Mehryar Mohri 的《Foundations of Machine Learning》(2012)。这是一本从统计学习角度切入 ML 的好书。如果你对 ML 算法的统计学保证感兴趣，比如 SVM 算法为什么有效及其样本……
Also consider Foundations of Machine Learning (2012) by Meryar Mohri. It is a very good book with a statistical learning approach to ML. If you're interested by statistical guarantees to ML algorithms, eg. why the SVM algorithm works and its sample…
我推荐 Shai Shalev-Schwartz 和 Shai Ben-David 的《Understanding Machine Learning: From Theory to Algorithms》[0]。我也用过 Mohri 的《Foundations of Machine Learning》，觉得非常有启发性 [1]。通常在这两本书之间，至少有一本的证明是容易读懂的。两本都收了吧，除非你只打算买一本，那种情况下就选 Shai Shalev-Schwartz 的。[0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...…
I recommend Shai Shalev-Schwartz and Shai Ben-David's Understanding Machine Learning: From Theory to Algorithms [0]. I've also used and found Mohri's Foundations of Machine Learning quite insightful [1]. Usually, between the two books, at least one's proof is easy to follow. Get both unless you're only getting one, in which case, get Shai Shalev-Schwartz's. [0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...…
…别人的库，但如果你要自己开发算法，或者想认真钻研学术文献，就必须搞懂这里面的内容。我考虑的其他主要书籍是《Foundations of Machine Learning》（Rostamizadeh、Talwalkar 和 Mohri 合著）以及《Machine Learning: A Probabilistic Perspective》（Murphy 著）。还有另外几本，但我觉得除非有很充分的理由，否则没必要避开这三本。
…somebody else's library, but if you're developing your own algorithms or you want to engage seriously with the research literature, you have to know what's in here. The main other books I'd consider are Foundations of Machine Learning (Rostamizadeh, Talwalkar & Mohri) and Machine Learning: A Probabilistic Perspective (Murphy). There are a few others, but I think you'd need a good reason not to pick one of those three.
…这些书里我推荐 Bishop 的《Pattern Recognition and Machine Learning》。我通读过这本书，结构排布和逻辑推导都非常出色。如果是更偏重数学深度的 ML 内容，我推荐 Mohri 的《Foundations of Machine Learning》。至于其它各方面的参考书，我推荐 Murphy 的《Machine Learning: A Probabilistic Perspective》。如果想深入了解图模型，可以去看看《Probabilistic Graphical Models: Principles…
…those I'd suggest "Pattern Recognition and Machine Learning" by Bishop. I've read throughout this and it's really well organized and thought out. For more mathematically advanced ML stuff I'd suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles…
…(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693...) 统计/理论方向：Shai Shalev-Schwartz 和 Shai Ben-David 的《Understanding Machine Learning》(http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...) Mohri 的《Foundations of Machine Learning》(https://cs.nyu.edu/~mohri/mlbook/) 上面两门课可以共用 SSS 的在线学习讲义（https://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf）。讲真，大多数优化算法的随机变体……
…(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693...) Statistical/Theoretical: Shai Shalev-Schwartz & Shai Ben-David's Understanding Machine Learning (http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...) Mohri's Foundations of Machine Learning (https://cs.nyu.edu/~mohri/mlbook/) The two above courses could share SSS's Online Learning text (https://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf). To be fair, the stochastic variants of most optimization…
也可以考虑 Mehryar Mohri 的《Foundations of Machine Learning》(2012)。这是一本从统计学习角度切入 ML 的好书。如果你对 ML 算法的统计学保证感兴趣，比如 SVM 算法为什么有效及其样本……
Also consider Foundations of Machine Learning (2012) by Meryar Mohri. It is a very good book with a statistical learning approach to ML. If you're interested by statistical guarantees to ML algorithms, eg. why the SVM algorithm works and its sample…
我推荐 Shai Shalev-Schwartz 和 Shai Ben-David 的《Understanding Machine Learning: From Theory to Algorithms》[0]。我也用过 Mohri 的《Foundations of Machine Learning》，觉得非常有启发性 [1]。通常在这两本书之间，至少有一本的证明是容易读懂的。两本都收了吧，除非你只打算买一本，那种情况下就选 Shai Shalev-Schwartz 的。[0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...…
I recommend Shai Shalev-Schwartz and Shai Ben-David's Understanding Machine Learning: From Theory to Algorithms [0]. I've also used and found Mohri's Foundations of Machine Learning quite insightful [1]. Usually, between the two books, at least one's proof is easy to follow. Get both unless you're only getting one, in which case, get Shai Shalev-Schwartz's. [0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...…
* 《Foundations of Machine Learning》 * 《All of Statistics》 * 《Doing Bayesian Data Analysis》 还有 Coursera 上的 ML specialization
* Foundations of Machine Learning * All of Statistics * Doing Bayesian Data Analysis Also the ML specialization on Coursera
…别人的库，但如果你要自己开发算法，或者想认真钻研学术文献，就必须搞懂这里面的内容。我考虑的其他主要书籍是《Foundations of Machine Learning》（Rostamizadeh、Talwalkar 和 Mohri 合著）以及《Machine Learning: A Probabilistic Perspective》（Murphy 著）。还有另外几本，但我觉得除非有很充分的理由，否则没必要避开这三本。
…somebody else's library, but if you're developing your own algorithms or you want to engage seriously with the research literature, you have to know what's in here. The main other books I'd consider are Foundations of Machine Learning (Rostamizadeh, Talwalkar & Mohri) and Machine Learning: A Probabilistic Perspective (Murphy). There are a few others, but I think you'd need a good reason not to pick one of those three.