Statistical Learning with Big Data

An AARMS summer school course

Instructors: Hugh Chipman (Acadia University) and Xu (Sunny) Wang (St. Francis Xavier University)

Tuesday August 5: Assignment 2 solutions posted.
Friday, August 1: Assignment 3 is available in the "Assignments and solutions" folder.
Thursday july 31: Mark's R Markdown code is the file "RMarkdown_example.Rmd" in the "R" folder. I've also added my .Rmd file for the second part of my lecture today in the R folder. The marked up pdf source is in the regular course documents folder.
All R code in the book's "Lab" sections is online at http://www-bcf.usc.edu/~gareth/ISL/code.html. I've also noted this in "Other R resources".
Wednesday July 23 - grading scheme changed, see revised outline in "docs" folder.
Posts on Tuesday: Ch 3 supplement (4:30pm), R code for Ch3 (1:30pm, then updated 4:30pm), minor changes to assignment 1 (4:30pm).
Lecture for Ch 3 posted (Mon night). I plan to add a few extra pages at the end, still.
By request, I've added a folder with R code used in the class. The only example there now, "0102.R" is quite advanced and will not be needed for the assignment.
Monday lecture with annotations is posted. I'll update these daily after class.
On Monday afternoon (July 21), we will have a lab (Dunn 301a), with this schedule:

1:00pm - 2:00pm Network access, installing R, installing RStudio
2:00pm - 3:30pm Tutorial: section 2.3 of ISLR, plus extra notes in "02Rtutorialsupplement.pdf" posted in "course documents" folder.

If you have successfully installed R and RStudio, and can access the internet through Dalhousie's networks, then you can come at 2pm. If you need help with any of these tasks, it is recommended that you come between 1 and 2.
Assignment 1 is posted in the newly created "assignments and solutions" folder (see links above).
July 18: Notes for Monday are now in the "course documents" folder (0102.pdf, for chapters 1 and 2). I will mark up the notes in class and then re-post the modified version after the lecture. You don't need to do anything before the lecture, you can just follow along on Monday.
June 10: Want to get an early start? See the outline for information on textbook (it's free online, if you want hardcopy you should buy it) and software (R and RStudio).
June 10: Course outline posted (pdf).