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