AI FOR EVERYONE
Machine Learning (ML) is making a significant impact on the productivity and competitive edge of organizations. Driving market forces now and in the future are products and processes that are faster, smarter, and more personalized.
More than just data analysis and computer science skills, the ML for Business Strategy microcredential courses are designed for university students, recent graduates, and professionals interested in understanding artificial intelligence (AI) and machine learning principles and how to apply them across various sectors.
These courses will help you both understand the capacity of AI and ML and feel confident applying insightful, cutting-edge and dynamic learnings into your careers and making big impacts in your organizations.
Machine Learning Microcredenital Courses
Business Mapping, ML Canvas, and ML Data Strategy are microcredential courses, made possible by funding from the Government of Alberta. Upon successful completion of a course, students will receive a Concordia University of Edmonton Certificate of Completion.
Machine Learning (ML) for Business Strategy — Business Mapping
The ML for Business Strategy — Business Mapping course will teach you the potential of ML in strategic business planning and provide you with the resources to create an action plan to move forward – without needing to code or program.
“The course content was just the right level for a beginner in machine learning. The instructor had industry background and was available outside the classroom hours for support.”
Past Business Mapping Student
Saturdays
Online Delivery
No Prerequisites
Course Schedule
| START/END | DAY | TIME | HOURS | FEES |
| Oct 29 – Dec 10, 2022 | Saturdays | 10:00 a.m. – 11:30 a.m. | 21 hours | $600 + GST |
Online delivery: Hybrid 1.5 hours synchronous online + 1.5 hours of asynchronous online per week.
Course Outcomes
By the end of this module, learners will be able to:
- Design an action plan to strategically implement ML in business
- Familiarize with the technical elements of ML, without needing to code or program
- Understand the potential of ML in strategic business planning
- Expand their network by connecting to a community of like-minded professionals who are successfully deploying ML in their organizations
Lecture Topics
Introduction to ML: Introduction to ML and its growing role in business.
Implementing ML in a business: Understanding the application of ML, the role of data, and the importance of an implementation plan.
ML related computer languages: Explore the requirements for the application of ML from a computer language perspective.
Case studies: Explore the case studies in areas such as banking, insurance, social innovation, sensor optimization etc.
ML challenges and future: Develop an implementation plan for ML, and explore the future of ML in business.
Machine Learning (ML) for Business Strategy — ML Canvas
Machine Learning (ML) for Business Strategy — ML Canvas leverages industry collaboration and project-based learning to a framework to create a ML vision, facilitate collaboration, and identify key components for ML initiative implementation using an ML Canvas.
Wednesdays Evenings
Online Delivery
No Prerequisites
Course Schedule
| START/END | DAY | TIME | HOURS | FEES |
| Oct 19 – Nov 23, 2022 | Wednesday | 6:00 p.m. – 8:00 p.m. | 21 hours |
$300 + GST* |
Online delivery: Hybrid 1.5 hours synchronous online + 1.5 hours of asynchronous online per week.
*The course fee on the first offering of this course in the Fall 2022 semester is $300+GST. Subsequent offerings will be $600+GST.
CUE full time employees, students, and alumni please contact extension@concordia.ab.ca for information how to register in the first offering of this course for free.
Course Outcomes
By the end of this module, learners will be able to:
- Ideate, describe, evaluate and discuss a business model using the ML Model Canvas
- Understand how to use the ML Canvas framework and storyboards to evaluate the effectiveness of Customer Relationships and Channels
- Understand business types and how to evaluate key activities, resources, and partnerships in this context
Lecture Topics
ML Canvas: Learners will work through a ML Canvas. The Canvas is a tool to help business align on strategy, identify data requirements, sketch ideas, and define how to use ML in the business immediately.
ML implementation case studies: Learners will be introduced to case studies to understand the implementation of a ML Canvas based approach.
Machine Learning (ML) for Business Strategy — ML Data Strategy
The Machine Learning (ML) for Business Strategy — ML Data Strategy will help learners to understand major data principles, common pitfalls, and overall technology ethics so that they can operationalize informed and ethically responsible ML projects.
Wednesdays Evenings
Online Delivery
No Prerequisites
Course Schedule
| START/END | DAY | TIME | HOURS | FEES |
| Feb 7 – Mar 6, 2024 | Wednesday | 6:00 p.m. – 8:00 p.m. | 21 hours |
Free |
Online delivery: Hybrid 5 synchronous online lectures + 5 hours of asynchronous/recorded online lectures.
Course Outcomes
By the end of this module, learners will be able to:
- Examine the ability of algorithms to influence and decide human behavior in biased ways, and methods to avoid predictive bias
- Understand the role and importance of data, fundamental issues of fairness and bias ML
- Expand their network by connecting to a community of like-minded professionals who are successfully deploying ML in their organizations
Lecture Topics
Introduction to ML ethics: Introduction to rationale for studying ML ethics for businesses, identify applications and limitations of ML ethics.
Fairness in AI/ML: Introduction to a framework for approaching fairness in ML, including defining and checking for fairness and considerations when choosing between different fairness implementations.
Case studies with data: Explore steps and principles involved in building less-biased ML modules; Explore two classes of technique, data-based and model-based techniques for mitigating bias in ML and apply learnings on a course specific dataset.
Course Instructor
Rhys Chouinard
Director of Data Science at ATB Financial
About Rhys Chouinard
Rhys Chouinard began his journey in data science at the University of Alberta where he majored in Math and Astrophysics, before moving on to complete a Masters in Experimental Particle Physics. While trying to find dark matter with the DEAP-3600 collaboration, Rhys developed the skill set required for what was then emerging in industry under the name “data science.” After spending a fruitful eight years in academia learning the statistical, computational, and mathematical techniques of data science, Rhys undertook the challenge of applying his data-centric problem solving skills in industry. He mastered the art of applying machine learning to business problems during his four-year tenure in government as an economic analyst. In 2017, he joined ATB Financial as a Data Scientist, where he began applying machine learning techniques to solve problems in the data-rich realm of finance. He moved into the position of Director of Data Science in 2019, where he was charged with leading teams of data scientists to rapidly scale the value of the technology. Rhys current passion lies in teaching the niche techniques of applied machine learning to new generations of data scientists and leaders, in an effort to create a more intelligent and efficient future for humanity.
Questions?
Email us at extension@concordia.ab.ca