## StatLearning: Statistical Learning

 所在平台: Stanford Online 课程类别: 计算机科学

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#### 课程详情

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. The lectures cover all the material in by James, Witten, Hastie and Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website.

#### 课程评论(3条)

 1 52nlp 2014-04-14 18:51 1 票支持; 0 票反对 拿到了这门课程的证书，简单做一点补充。统计学习和机器学习到底有什么区别？学完这门课程最大的感受就是统计学习更强调从统计的角度来看问题和结果，当然这算是废话。打个比方，如果看目录，这门课程和其他的机器学习课程有很多重复相似的章节，但是也各有所侧重。一般的机器学习课程在介绍线性回归或者逻辑回归的时候会告诉你如何做预估以及预估的结果，而统计学习还会加一个置信区间，从统计的角度告诉你结果的可靠性...当然，这仅仅是一点区别，这门课程涵盖的范围很广，每周的课时量也还行，需要花很多时间，我个人由于介入的时间靠后，并且时间上投入的不是很足，勉强凑够了学分，所以有些内容理解的也不是很深刻。另外，把这门课程剩下的视频下载下来放到之前所存的网盘了，大家可以收藏保存：http://pan.baidu.com/s/1gd5hNdL
 1 52nlp 2014-02-06 15:27 1 票支持; 0 票反对 这门统计学习课程和凸优化课程节前备受大家关注，不过春节的缘故，我暂时放了放，节后回来选课时发现这门课程比较复合我的口味，参考教程是《An Introduction to Statistical Learning with Applications in R》，授课老师也是这本书的作者，并且电子版官方免费提供：http://www-bcf.usc.edu/~gareth/ISL/。同时时间还没有错过，老师们比较人性的考虑了大家可能有一些特殊事情耽误课程的问题，统一把作业截止时间调整为2014年3月21号，只要这个时间之前完成作业，都可以算数。 另外之前在“公开课可下载资源汇总” 帖子下有同学询问这门课程的视频下载情况，所以顺手把目前4讲的视频下载了下来（其实注册课程后，每个课程视频下都有下载链接），统一放到百度网盘上了：Stanford Statistical Learning 2014，需要的同学可以考虑收藏，回头完成这门课程之后再来写课程点评。
 0 王枷淇_ghostwang 2014-01-21 10:23 1 票支持; 1 票反对 期待！

#### 课程简介

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).