Applied Artificial Intelligence & Machine Learning
(Optional Co-op)

• Hours: 28
• Credits: 2
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  The cost is included in the course fee. View the eText fee.

• Hours: 1
• Credits: 0
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

This course introduces students to the mathematical foundations behind Artificial Intelligence. In addition to mathematical principles, context is provided around the development and evolution of AI algorithms throughout history, including an overview of Game Theory.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

Many strategies and techniques are explored during this course that can be applied to machine learning workloads. Students will gain important background information and practical implementation experience on software design and development techniques and how they have evolved over the years.

• Hours: 56
• Credits: 4
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

An important component of application and organizational security is the understanding of the rights and obligations of both the individual and the organization with respect to privacy and intellectual property. In this course students will explore ethics and law, including both Canadian law and the law of other jurisdictions, for the ways ethics and law inform the best practices of security professionals and organizations. Students will learn to assess the implications of new application development as well as the choice of supporting technologies, such as cloud computing, with respect to both vulnerabilities and liabilities of an organization. Students in this course will discuss topics such as Big Data, organized hacking, government surveillance, and industrial espionage.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

Students will enhance their existing programming skills through the introduction of programming platforms like Python to develop and resolve challenges in Machine Learning software development.

• Hours: 56
• Credits: 4
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

Data Analysis and Modeling fundamentals are rooted in traditional statistics and advanced statistical models. Students will begin this course by reviewing ways a common tool (like a spreadsheet) provides powerful statistical analysis tools. Then, students will implement formulae and algorithms used for data and statistical analysis using Python and libraries for data analysis and manipulation. Finally, students will examine topics in predictive and prescriptive modeling.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

Adopting new software technologies is a risky proposition in many organizations, thankfully several styles of rapid prototyping and agile software development give us the flexibility to quickly evolve designs and evolve the fit of an emerging software prototype to meet the needs of early, exploration phases of adoption. Working as members of a software development team, students will gain practical experience and guidance to design and develop an effective software prototype.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

• Hours: 14
• Credits: 1
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

Software development techniques are expanded on in this course which provides deeper coverage of machine learning implementations.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

This course studies the implementation of AI in support of organizational decision making. With an overview of historic approaches to Decision Support Systems (DSS) the pathway to today's predictive and autonomous applications for a range of industries is explored.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

This course brings students through a moderated series of investigations into emerging and important trends related to AI and ML. Students share and discuss their findings on a range of related topics while developing and enhancing communications proficiencies.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

This project course challenges students to fully apply the skills they have developed during this program towards the realization of a capstone project in AI and ML.

• Hours: 126
• Credits: 7
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

The fourth wave of industrialization, the era of ‘smart manufacturing’ is well underway. In this course students will study applications of AI and ML within the industrial and manufacturing spaces. With applications ranging from precision agriculture, autonomous manufacturing, deconstruction of e-waste, and even more applications, students learn to assess various hardware and software platforms helping to shape the manufacturing landscape of tomorrow.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.