Practical Statistical Learning in R

CS 598/STAT 542: Practical Statistical Learning was my first ever 500 level course and probably the most challenging course I have ever taken. There were some assignments that I spent over 20 hours on. The challenging part was working through the vague directions from the instructor. I continued to hone my skills on working in a large class group over Slack. I was able to come out of it with an A.

This course covered two of the best texts for Machine Learning:

• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. Commonly referred to as ISLR
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Commonly referred to as ESL

The course website was well laid out and the class notes are worth referring back to.

The Homeworks covered:

1. KNN and Linear Regression
2. Model and Variable Selection in Linear Regression
3. Nonlinear Regression and Kernels
4. Tree and Random Forests
5. Clustering Methods
6. Classification and SVM
7. Boosting

There were two projects at the end. An individual and group project.

I was able to maintain my streak of all A’s so far in this program.