Certificate in Artificial Intelligence

Faculty of Engineering Graduate Certificate in Artificial Intelligence


Learn AI before AI learns you

As artificial intelligence is adopted by businesses across almost every industry as a means to unlock value, the future of business, and certainly society at large, teems with new possibilities. With the new Certificate in Artificial Intelligence, you can learn the essential skills to grapple with current and future challenges faced by Alberta industry.

By completing this certificate, you will be able to recognize ways to improve efficiencies or transform current technology or industrial processes through artificial intelligence. Courses will focus on experiential learning using industry relevant projects. You will be expected to work in and lead project teams in learning activities that are relevant and transferable to your current work.

Learn while still working!

You can complete this certificate easily with limited time away from work. Each of the three courses is offered through a blend of online and in-person classes over six-weeks. Classes will be held on Friday afternoons and Saturdays.


$1950 per course

Course fees are due one week prior to the course starting.

Courses must be taken in sequence, but applicants have two years to complete the certificate.

Interested in Fall 2020?

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Have questions?

Please contact epdc@ualberta.ca if you have any questions.


2020/21 Schedule

AIE 1: Machine Learning Applications

This course offers an introduction to a variety of unsupervised and supervised methods of data processing. Learn different architecture configurations for predictive modeling, kernel methods, neural networks, and techniques for evaluation of model performance. You'll bring real-world problems from your own workplace, and use machine learning to solve them. With access to the state-of-the-art resources in the Faculty of Engineering, and leading researchers in the area, your learning will be hands-on and practical with application to industry.

AIE 2: Applications with Deep and Graphical Networks

Dive into Deep Learning methodology and begin to build neural networks. The course will cover subjects such as convolutional neural networks and their applications to images; recurrent network models for processing natural language and speech. It will also introduce networks representing probability distributions, in particular Bayesian and Markov networks, and their applications.

Prerequisite AIE 1.

AIE 3: Reinforcement Learning Applications

An introduction to principles of reinforcement learning that include algorithms supporting action decision processes that optimize long-term performance. Topics include: dynamic programming, Q-learning, Monte Carlo reinforcement learning, and efficient algorithms for single- and multi-agent planning.

Prerequisite AIE 2.