Statistical Learning

Stanford University

Disclaimer

This is from the self-paced online learning course at Stanford University. I took this course to refresh my statistical knowledge and the materials are mostly involved in machine learning techniques. The output of this course is to understand concepts and theories of statistics in order to interpret the data, and analyze their insights appropriately.

Materials

• The same books that I posted in Machine Learning course.

Through the course, I did some notes for quizzes and code in R for implementation session. Please check the notes and code as the following links:

Notes

• Chapter 2 - Overview of statistical learning

• Chapter 3 - Linear Regression in R and its note

• Chapter 4 - classification Problems

• Chapter 5 - Resampling methods

• Chapter 6 - Linear Model Selection and regulization

• Chapter 7 - Moving beyond linearity

• Chapter 8 - Tree-based

• Chapter 9 - SVM

• Chapter 10 - Unsupervised learning

R session

• Link

R Misc. Resources:

• An Introduction to R

• R for Beginners

• Using R for Introductory Statistics

• Modern Applied Statistics with S

• Practical ANOVA and Regression in R

• simpleR

• Introduction to R

• R Reference Card

• R Manuals

Claims

I completed the course on January 20, 2018.

ID#: 300b0d4031844c758d7645e39d1ad079

Statement URL online or offline