This is the GitHub home page for the 2019/2020 iteration of the course BAIT 509 at the University of British Columbia, Vancouver, Canada. Please see the syllabus for more information about the course. Current students should refer to the UBC Canvas course website for the most up-to-date content and announcements.

This repository is avaiable as an easy-to-navigate website.

By the end of the course, students are expected to be able to:

1. Describe fundamental machine learning concepts such as: supervised and unsupervised learning, regression and classification, overfitting, training and testing error;
2. Broadly explain how common machine learning algorithms work, including: naïve Bayes, k-nearest neighbors, decision trees, support vector machines, and ensemble methods;
3. Implement a machine learning pipeline (i.e., loading data, creating a machine learning model, testing the model) in Python; and,
4. Apply and interpret machine learning methods to carry out supervised learning projects and to answer business objectives.

At your service!

Details about class meetings will appear here as they become available. Optional additional material is also available for each lecture.

All assessments will be submitted through UBC Canvas.

Want to talk about the course outside of lecture? Let's talk during these dedicated times.

• An Introduction to Statistical Learning with R
• Data Mining: Practical Machine Learning Tools and Techniques
• A Visual Introduction to Machine Learning
• A Course in Machine Learning