From f972778d62aa7d83df28a273331d478f89ac3a28 Mon Sep 17 00:00:00 2001 From: Prakhar Srivastav Date: Fri, 17 Jul 2015 19:34:06 +0300 Subject: [PATCH] Added ISL course --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1d2dbee..9be586f 100644 --- a/README.md +++ b/README.md @@ -478,7 +478,7 @@ Courses - [StatLearning](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) **Intro to Statistical Learning** *Stanford University* Assignments Lecture Notes Readings Lecture Videos - 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. - - The lectures cover all the material in [An Introduction to Statistical Learning, with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) which is a more approachable version of the [Elements of Statistical Learning]()http://statweb.stanford.edu/~tibs/ElemStatLearn/ (or ESL) book. + - The lectures cover all the material in [An Introduction to Statistical Learning, with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) which is a more approachable version of the [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) (or ESL) book. - [11-785](http://deeplearning.cs.cmu.edu/) **Deep Learning** *Carnegie Mellon University* Assignments Readings - The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. *Lectures* and *Reading Notes* are available on the page. - [10-601](http://www.cs.cmu.edu/~ninamf/courses/601sp15/) **Machine Learning** *Carnegie Mellon University* Assignments Lecture Notes Readings Lecture Videos