BAIT509 - Business Applications of Machine Learning#

This is the home page for BAIT 509 at the University of British Columbia. This course is and introduction to machine learning concepts, such as model training, model testing, generalization error and overfitting. Exposure to a variety of machine learning techniques, with deeper exploration of a few chosen techniques. Forming good scientific questions to address business objectives with machine learning. Python will be the primary programming language used.

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.

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

Office hour

Quan Nguyen

Instructor

quan.nguyen@ubc.ca

Thursday 12:00-13:00 on Zoom

Armin Saadat Boroujeni

TA

Ailar Mahdizadeh

TA

Mohammad Mahdi Asmae

TA

Harsh Sharma

TA

Julian Camilo Becerra Leon

TA

Meltem Omur

TA

Class Meetings#

#

Topic

Link

Interactive applets

1

Introduction to machine learning and decision trees

Lecture 1

Decision tree

2

Splitting and Cross-validation

Lecture 2

Train-test-validation, Cross-validation

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

Jan 20

20%

Assignment 2

Jan 27

20%

Quiz

Jan 31

10%

Assignment 3

Feb 10

20%

Final Project

Feb 17

30%

All assessments will be submitted through UBC Canvas.

Supplementary Resources#