书籍介绍
在浩如烟海的机器学习教材里，这本书之所以被反复推上神坛，并非因为它讲得多
作者简介
Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.
AI导读
核心看点
- 全面介绍贝叶斯方法在机器学习中的应用
- 将模式识别与机器学习统一于概率框架下
- 涵盖图形模型及变分贝叶斯等前沿技术
读者共识
- 被誉为机器学习领域的圣经级经典教材
- 理论深度极高，适合查阅而非快速通读
- 部分读者反映逻辑跳跃，需一定数学功底
精彩摘录
- "Clearly, there are many possible probabilistic structures that can be constructed according to the needs of particular applications. Graphical models provide a general technique for motivating, describing, and analysing such structures, and variational methods provide a powerful framework for perfor"
- "A further issue in finding maximum likelihood solutions arises from the fact that for any given maximum likelihood solution, a K-component mixture will have a total of K! equivalent solutions corresponding to the K! ways of assigning K sets of parameters to K components."
- "The average amount of information that they transmit in the process is obtained by taking the expectation with respect to the distribution p(x) ."
- "Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years."
- "Because the error function is a quadratic function of the coefficients w, its derivatives with respect to the coefficients will be linear in the elements of w, and so the minimization of the error function has a unique solution, denoted by w, which can be found in closed form."
- "RSS(β) is a quadratic function of the parameters, and hence its minimum always exists, but may not be unique."
- "One way to view a linear classification model is in terms of dimensionality reduction."
- "... the internal nodes represent deterministic variables rather than stochastic ones."
目录
1 Introduction 1
1.1 Example: Polynomial Curve Fitting . . . . . . . . . . . . . . . . . 4
1.2 Probability Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2.1 Probability densities . . . . . . . . . . . . . . . . . . . . . 17
1.2.2 Expectations and covariances . . . . . . . . . . . . . . . . 19
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用户评论
毫无疑问，PRML实乃入门必读之圣书！！！花了一周时间又把公式推了一遍，欲罢不能。另推：David Barber 2012出的Bayesian Reasoning and Machine Learning，其中的Approximate inference部分比PRML讲的好并详述一些最新进展，讨论了几种bound之间的tightening关系。如果想要了解Advanced一点的topic，还可以看Kevin Murphy新出的那本，囊括了更多近年的hot topic入门简介包括deep learning。btw，Kevin现在已经离开UBC，跑到google做knowledge graph，对下一代搜索引擎的query语义理解很有帮助，B厂内部也刚开始无声无息的做这方面的项目。
只读了前几章...
机器学习的好教材，较深入
比Murphy那本好读的多
很好的书
期待影印版
打印看太吃力了
不知道为什么就是很难读进去，找了译本也还是这样。
读完以后难免自己也变成了贝叶斯信徒
🙄️ 🙄️
这本书整体是按照贝叶斯来写的，看完这本书并且刷完习题，机器学习三大顶会NIPS、ICML、ICLR上的论文就可以无障碍阅读了
I survived!
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