Ryerson University’s Department of Electrical, Computer and Biomedical Engineering offers a master’s degree (MEng) in Electrical and Computer Engineering with specialization in AI that provides students with the technical training and ethical awareness they need to be successful professionals in the AI field.

Nature of the Program

The current capacity of the program is 50 full-time master’s students per year. Admission is based on excellence and the following requirements:

  • Completion of a four-year bachelor’s degree in a related field
  • Minimum GPA or equivalent of 3.00/4.33 (B) in the last two years of study
  • A statement of interest in AI specialization demonstrating a capacity to succeed in the program

Students admitted to the program will be required to take four core courses:

  1. Intelligent Systems (EE8209)
  2. Neural Networks (EE8204)
  3. Deep Learning (EE8603)
  4. Advanced Data Engineering (EE8605)

Four interdisciplinary electives will also be required in the areas of energy, sustainability, computer networks, digital media and urban development—which will be studied through an AI lense. Importantly, a course on the ethical implications of AI will also be offered (EE8010), covering topics such as inherent bias, fairness and accountability in research and application.

Vector Scholarships In Artificial Intelligence (VSAI)

The Vector Institute will award up to 115 scholarships to meritorious students who pursue a full-time AI-related master’s degree in the Province of Ontario in the 2019-20 academic year. Scholarships are valued at $17,500 CAD for one full year of study. To be considered for a scholarship, students must hold a GPA equivalent to a first class standing (A-) and take the following actions:

  • Apply and be accepted for full-time study in an AI-related master’s program for the 2019-20 academic year in the province of Ontario that is:
    • A) recognized by the Vector Institute; or
    • B) in an AI-related discipline that offers individualized study paths that are demonstrably AI-focused
  • Submit a 250-word (maximum) statement outlining the reason for pursuing a master’s in AI, relevant AI-related experience, and career aspirations. Statements must be submitted directly to the program to which the student has applied and which has offered the student admission.
  • Acquire references from two referees, one of whom must be an academic. Reference forms must be submitted directly by the referee to the administrator of the program to which the student has applied and which has offered the student admission. Students must complete the top part of the form BEFORE emailing it to their referees.

Students must be admitted for the 2019-20 academic year by March 22nd, 2019 to the full-time Master of Engineering with AI Concentration program which is a recognized program by the Vector Institute. Programs must submit nominations to the Vector Institute by April 5, 2019. Scholarship decisions will be announced on April 26, 2019.

Skills, Competencies and Experience Acquired By Graduation

Upon completion of the program, our graduates will be able to identity the requirements of an AI-driven system, analyze state-of-the-art AI techniques and apply them in a range of disciplines in order to contribute solutions to many of industry and society’s greatest challenges. Specifically, our students will gain a deep understanding of:

  • Foundational aspects of machine learning, which covers the main concepts in data mining and statistical pattern recognition, including the core techniques in supervised and unsupervised classification.
  • Theoretical foundations and practical applications of artificial neural networks, including various forms of representation, training and evaluation.
  • State-of-the-art techniques in the area of deep learning, including deep convolutional neural networks, recurrent neural networks, and deep belief networks.
  • Actual applications of machine learning and statistical pattern recognition, including use cases in audio and video processing, and natural language processing.
  • How Big Data needs to be modeled, stored and retrieved within the context of distributed computing (MapReduce, Hadoop, Spark, noSQL technology and distributed stream-processing techniques).
  • How to develop scalable and distributed data management platforms that are essential for large- scale data analytics and machine learning projects.
  • Inherent biases of machine learning algorithms, algorithmic fairness, as well as accountability, and transparency in AI systems.

Careers Our Students Will Be Qualified For

The field of AI offers highly compensated, impactful employment opportunities across all services and sectors. Graduates of our program can expect to be qualified for many in-demand positions, including:

  • Machine learning engineer
  • Data scientist
  • Research scientist
  • R&D engineer
  • Business intelligence developer
  • Computer vision engineer
  • Big Data engineer/architect

Experiential Learning

One of Ryerson’s strengths is combining theory with experiential learning and applied research. As such, a practicum is included as a mandatory part of the program. The practicum will present students with a real-world problem and call on them to conceptualize, develop and test a prototype solution. The project will be completed in teams of two to three students who will work directly with their professor and industry partners. In addition to the experiential opportunity, students will also benefit from department-led training in teamwork, collaboration, and conflict resolution—skills that will be essential to their success in the workforce. An internship program is also in development that would give our students the opportunity to work with Vector-affiliated companies and health-care partners.

More Information and Contact Info

If you have questions about the master’s degree with an AI concentration at Ryerson’s Department of Electrical, Computer and Biomedical Engineering, email us at gradinfo@ee.ryerson.ca. For more information about graduate studies at Ryerson University, visit www.ryerson.ca/feas