Statistical Learning – 2016

December 12, 2015
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(This article was first published on R – Discovering Python & R, and kindly contributed to R-bloggers)

On January 12, 2016, Stanford University professors Trevor Hastie and Rob Tibshirani will offer the 3rd iteration of Statistical Learning, a MOOC which first began in January 2014, and has become quite a popular course among data scientists. It is a great place to learn statistical learning (machine learning) methods using the R programming language. For a quick course on R, check this out – Introduction to R Programming

Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.

The course covers the following book which is available for free as a PDF copy.

Logistics and Effort:

statLearnEffort

Rough Outline of Schedule (based on last year’s course offering):

Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2)
Week 2: Linear Regression (Chapter 3)
Week 3: Classification (Chapter 4)
Week 4: Resampling Methods (Chapter 5)
Week 5: Linear Model Selection and Regularization (Chapter 6)
Week 6: Moving Beyond Linearity (Chapter 7)
Week 7: Tree-based Methods (Chapter 8)
Week 8: Support Vector Machines (Chapter 9)
Week 9: Unsupervised Learning (Chapter 10)

Prerequisites: First courses in statistics, linear algebra, and computing.

 

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