Jan 17:
Jan 19:
Jan 24:
Jan 26:
Before class:
Chapter 2 - Statistical Learning — Take notes in Rmarkdown
In class quiz 01
Jan 31:
Lab 02
Start: Reproduce Lab: Modified Introduction To R.
Submit your html file and Rmd file Lab 2, no later than 5 pm Feb 9th.
Feb 2:
Chapter 2 - Statistical Learning — Take notes in Rmarkdown
Continue to work on Chapter 02 Lab (Due Thursday - Feb 9th)
In class quiz 02
Feb 7:
Feb 9:
Chapter 3 - Linear Regression — Take notes in Rmarkdown
Before class:
In class quiz 03
Lab 02
Feb 14:
Feb 16:
Before class:
Chapter 3 - Linear Regression — Take notes in Rmarkdown
In class quiz 04
Feb 21:
Feb 23:
Chapter 3 - Linear Regression — Take notes in Rmarkdown
In class quiz 05
Feb 28:
March 2:
Chapter 3 Lab
Start: Reproduce Lab: Linear Regression.
Submit your Chapter 3 Lab html file and the Rmd file, no later than 5 pm March 21st.
March 7:
Before class:
Chapter 3 - Linear Regression — Take notes in Rmarkdown
March 9:
Before class:
Chapter 3 - Linear Regression — Take notes in Rmarkdown
In class quiz 06
March 14-16:
March 21:
Reminder: Chapter 3 Lab
March 23:
Chapter 3 - Linear Regression — Take notes in Rmarkdown
Exam 1 is Thursday next week!
In class quiz 07
March 28:
Project 01
Submit your html and Rmd files no later than 5 pm April 11th.
March 30:
April 4:
April 6:
Chapter 5 - Resampling Methods — Take notes in Rmarkdown
In class quiz 08
April 11:
Chapter 5 - Resampling Methods — Take notes in Rmarkdown
Project 1 due today
Chapter 5 Lab
Start: Reproduce Chapter 5 Lab.
Submit your Chapter 5 Lab html file and the Rmd file, no later than 5 pm April 18th.
April 13:
Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown
In class quiz 09
April 18:
Chapter 5 Lab
April 20:
Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown
In class quiz 10
Reminder:
April 25:
Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown
Project 2: Start Here
Turn in Project 2 Predictions- NLT May 2nd, 5:00 pm
Send an email to the instructor using a subject of “House Data Predictions” with an attached vector (csv file) of your predictions using the data set housedataT.csv. Note: your vector should contain 4229 predictions.
Turn in Project 2 - NLT May 6th, 5:00 pm
Make sure a single .Rmd and a single .html with the sections outlined in the Example Paper is in your Project2 emailed to me.
Also turn in the all the model you have tried as a single .Rmd and a single .html file. Section names should read for example: Model 1: Forward selection model. A brief description of the model should be included as well.
April 27:
May 2: