Statistical Learning About This Course 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. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). Course Staff Trevor Hastie Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Rob Tibshirani
Beginner's guide to R: Introduction R is hot. Whether measured by more than 4,400 add-on packages, the 18,000+ members of LinkedIn's R group or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? Because it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That also makes it easier for others to validate research results and check your work for errors -- an issue that cropped up in the news recently after an Excel coding error was among several flaws found in an influential economics analysis report known as Reinhart/Rogoff. The error itself wasn't a surprise, blogs Christopher Gandrud, who earned a doctorate in quantitative research methodology from the London School of Economics. Sure, you can easily examine complex formulas on a spreadsheet. Indeed, the mantra of "Make sure your work is reproducible!" Why not R?
Learn R Upload mybringback.com mybringback Loading... Working... ► Play all Learn R mybringback23 videos17,852 viewsLast updated on Jun 26, 2014 Play all Sign in to YouTube Sign in History Sign in to add this to Watch Later Add to Loading playlists... Code School - Try R MIT Why R is Hard to Learn by Bob Muenchen R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. If you have experience with other analytics tools, you may at first find R very alien. Below is a list of complaints about R that I commonly hear from people taking my R workshops. Unhelpful Help R’s help files are often thorough and usually contain many working examples. Another confusing aspect to R’s help files stems from R’s ability to add new capabilities (called methods) to some functions as you load add-on packages. So an R beginner has to learn much more than a SAS or SPSS beginner before he or she will find the help files very useful. Misleading Function or Parameter Names The most difficult time people have learning R is when functions don’t do the “obvious” thing. Another command that commonly confuses beginners is the simple “if” function. Too Many Commands
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