Concepts in AI Algorithms

By Garry Ka Lok Chu
Cohort 2020-2021

First of all, I would like to thank Dawson College for giving me the opportunity to learn about different aspects of AI that are relevant to Mathematics. Throughout my journey as an AI Teaching Fellow, I have learned a lot from reading papers and watching educational videos. Also, thanks to my exchanges with the other AI Teaching Fellows (who are members of numerous departments other than mine), I came to understand different perspectives on AI.  Although my mandate as a Fellow has now ended, education is a continuous learning process and I will continue to learn more about AI and will serve as an ambassador for AI from the perspective of Mathematics.

The materials found in this portfolio can be used in any Dawson Mathematics course, especially courses related to distances as well as those related to probability and statistics, such as the following:

201-016-RE        Remedial Activities For Secondary IV Mathematics

201-015-RE        Remedial Activities For Secondary V Mathematics

201-105-DW       Linear Algebra

201-401-DW       Statistics for Social Science

201-922-DW       Introduction to Statistical Methods for Chemical Technology

201-BZS-05        Probability and Statistics

201-NYC-05        Linear Algebra


Also, I have started developing two courses:

75-hour option course                        Introduction to Machine Learning

45-hour complementary course           Solve AI Mystery Using Spreadsheets


Modules on AI Algorithms
  1. Regression
  2. Decision Tree
  1. Naïve Bayes
  1. K Nearest Neighbors
  1. K-Means Clustering


Presentation on Ped Day

Date: October 14, 2020

Title: Are AI-Powered Exam Proctoring Systems the Answer We Have Been Waiting For?

Abstract: To what extent can we detect and prevent cheating in exams through remote AI proctors? In this session, we will explore what is currently available, past experiences, and potentials for the future. We will highlight the practical and ethical concerns around monitoring students physically, as well as tracking their private data. We will also explore some alternatives to reduce cheating in exams.

Presenters: Jennifer Sigouin, Vanessa Gordon, Garry Chu, Carl Saucier-Bouffard, Ahmad Banki

(Slides Used during 2020 Ped Day)


Presentation on Pi DayPicture2

Date: March 12, 2021

Title: Concepts in AI Algorithms

Abstract: Do you know how machines sort emails? Can Toffoli score in his next NHL game? Is there any ethical issue in AI algorithms? We will explore these AI concepts together in this talk.

Presenter: Garry Chu


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  1. Hong Zhou (2020) Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Method.
  2. Jiawei Han, Micheline Kamber and Jian Pei (2012) Data Mining: Concepts and Techniques, 3rd Edition.


  1. Hanif Bhuiyan, Akm Ashiquzzaman, Tamanna Islam Juthi, Suzit Biswas & Jinat Ara (2018) A Survey of Existing E-Mail Spam Filtering Methods Considering Machine Learning Techniques. Available at:


  1. Emmanuel Gbenga Dada, Joseph Stephen Bassi, Haruna Chiroma, Shafi’i Muhammad Abdulhamid, Adebayo Olusola Adetunmbi & Opeyemi Emmanuel Ajibuwa (2019) Machine learning for email spam filtering: review, approaches and open research problems. Available at:


  1. Victoria Rodriquez, Karan Sharma & Dana Walker (2018) Data Breast Cancer Prediction with K-Nearest Neighbor Algorithm using Different Distance Measurements. Available at:


Videos & Documentaries
Lectures on Machine Learning Algorithms


AI Ethics
  • Artificial Intelligence and Algorithms: pros and cons (42 minutes)
    Tilman Wolff and Ranga Yogeshwar (September 26, 2019) The Great Leap Forward. [Online video] Available at:


Bias in AI Algorithms


Future of Jobs