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 Ramandi

TA

ramandi18@gmail.com

Class Meetings

Details about class meetings will appear here as they become available.

#

Topic

Link

1

Introduction to machine learning and decision trees

Lecture 1

2

Splitting and Cross-validation

Lecture 2

3

Baseline, k-Nearest Neighbours

Lecture 3

4

SVM with RBF Kernel and Feature Preprocessing

Lecture 4

5

Preprocessing Categorical Features and Column Transformer

Lecture 5

6

Naive Bayes and Hyperparameter Optimization

Lecture 6

7

Linear Models

Lecture 7

8

Business Objectives/Statistical Questions and Feature Selection

Lecture 8

9

Classification and Regression Metrics

Lecture 9

10

Multi-Class, Pandas Profiling, Finale

Lecture 10

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 26th 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

Fridays 12-1 pm

Zoom link in Canvas

Andy Tai

Wedesdays 4-5 pm (Starting April 28th)

Zoom link in Canvas

Daniel Ramandi

In class; Monday and Wednesdays 8-10 am

Zoom link in Canvas