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BAIT509 - Business Applications of Machine Learning

This is the GitHub home page for the 2020/2021 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 available as an easy-to-navigate website.

Learning Objectives

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/validation/testing error, parameters and hyperparameters, and the golden rule.
  2. Broadly explain how common machine learning algorithms work, including: naïve Bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression.
  3. Identify when and why to apply data pre-processing techniques such as scaling and one-hot encoding.
  4. Use Python and the scikit-learn package to develop an end-to-end supervised machine learning pipeline.
  5. Apply and interpret machine learning methods to carry out supervised learning projects and to answer business objectives.

Teaching Team

Name Position email
Hayley Boyce Instructor hfboyce@cs.ubc.ca
Ali Seyfi TA aliseyfi@cs.ubc.ca
Andy Tai TA andy.tai@mail.utoronto.ca
Daniel TA ramandi18@gmail.com
Name Position GitHub Handle
Hayley Boyce Instructor @hfboyce

Class Meetings

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

# Topic Link
1 Introduction to machine learning and decision trees Lecture 1 html/notebook
2 Splitting and cross-validation Lecture 2 html/notebook
3 KNN and SVM Lecture 3 html/notebook
4 Feature pre-processing Lecture 4 html/notebook
5 Naïve Bayes Hyperparameter optimization Lecture 5 html/notebook
6 Linear Regression/ Logistic Regression Lecture 6 html/notebook
7 Feature and Model Selection Lecture 7 html/notebook
8 Business questions and workflows Lecture 8 html/notebook
9 Classification and Regression Metrics Lecture 9 html/notebook
10 Topics related to the group project Lecture 10 html/notebook

Assessments

Assessment Due Weight
Assignment 1 April 28th at 23:59 20%
Quiz May 5th at 23:59 10%
Assignment 2 May 10th at 23:59 20%
Assignment 3 May 19th at 23:59 20%
Final Project May 29th at 23:59 30%

All assessments will be submitted through UBC Canvas.

Office Hours

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

Teaching Member When Where
Hayley Boyce Thursdays 1:00 -2:00 PST Zoom link in Canvas
Ali Seyfi TA TBD
Andy Tai TA TBD
Daniel TA TBD