This repo contains materials for the 2018 (inaugural!) iteration of BAIT509 at UBC. In particular, you can find:

• The syllabus can be found at syllabus.pdf.
• Class meeting notes can be found in the class_meetings folder.
• Instructions for your five assessments can be found in the assessments folder.

This repo does not contain things that really ought to be private. We'll use UBC Connect for these. This includes:

• Your grades and feedback on your assessments.
• Solutions to the assignments.

Since BAIT 509 is brand new, material in this repo will be under flux for the duration of the course. This is different from most courses, where you're only shown material when it's ready. NOTE: This means that you can see assessments before they're ready too, so don't start one unless you've been told it's ready! See below for more details.

This very manuscript is intended to help you navigate the course, kind of like a more practical version of your syllabus.

Here are the members of your teaching team. To see specifics of who's doing what in this course, see the staffing.md file.

As mentioned in Class Meeting 01, please use BAIT509 Issues as a primary mode of communication -- use email only for things that really should be private (find our emails in the syllabus).

We'll help you out! Copy and paste the following line to get the attention of the teaching team:

@vincenzocoia @vmasrani @mooahmed @rafizaman

Details about class meetings will appear here as they become available. Readings are optional, but should be useful.

Assessments can have one of three statuses:

• NA (Not Available): This assessment is not ready yet for you to begin.
• OPEN: This assessment is ready for you to work on.
• CLOSED: This assessment is due. We will not be accepting further work on this assessment.

A change in status will be accompanied with an Issue creation/comment, so you will be notified of it (if you're Watching the repo!)

Please submit your assignments to UBC Connect in the form of a jupyter notebook AND pdf file.

Here are some resources you might find useful for the course. Let me know if you find something you find useful, and I can add it here.

• An Introduction to Statistical Learning with R (aka ISLR).
  • A very well-written book covering a lot of concepts in supervised learning.
• Happy git and GitHub for the useR
  • Jenny Bryan's book on just about all the tools we're using in this course, and how they all work together. Doesn't include jupyter notebooks (?).