Master of Applied Science (MASc) with a Field of Study in Artificial Intelligence

Course work, research thesis

2 years

Funding available

Program Overview

Advancement in computing technology and the availability of big data has led to a resurgence of research in artificial intelligence (AI), machine learning and deep learning. AI is pushing the innovation boundaries across all disciplines, and industry has been investing heavily in various AI technologies. There is a high demand for graduates with an AI background from industry and research labs.

The Department of Electrical and Computer Engineering (ECE) has substantial expertise in AI-related methodologies and application areas. We prepare our MASc in AI graduates with area-specific knowledge, skills, and competencies sought by the AI sector.

Master of Applied Science program with a Field of Study in Artificial Intelligence provides graduate students with a solid foundation in the principles of AI, machine learning, and deep learning. Graduates will be able to design and analyze AI-related algorithms and methodologies, employ modern scientific and engineering tools, and apply AI-based techniques to tackle complex research problems. They will learn advanced research and technical knowledge in AI-related fields and will have a deep understanding of the ethical and societal implications of AI. The program will prepare graduates through a combination of classroom and online learning, team-based problem-solving and course projects, research seminars, and faculty-supervised research projects. The student learning experience will be promoted through an inquiry-based curriculum. The MASc program with a Field of Study in Artificial Intelligence is recognized by the Vector Institute for Artificial Intelligence as delivering a curriculum that equips its graduates with the skills and competencies sought by industry.

Program Structure

The program structure for the MASc program with a Field of Study in Artificial Intelligence is as follows:

Take a minimum of two courses from the list of AI-related courses, one of which is the mandatory graduate course ELEC 825
Take up to two more graduate courses as required in the MASc program
Complete an AI-related MASc thesis
Complete other requirements, including taking the graduate seminar course, ELEC 891, and the non-credit APSC 812 AI Ethics and Society course

AI-related courses:

ELEC 823 Signal Processing
ELEC 825 Machine Learning and Deep Learning
ELEC 829 Optimization for Machine Learning
ELEC 872 Artificial Intelligence and Interactive Systems
ELEC 874 Deep Learning in Computer Vision
ELEC 877 AI for Cybersecurity
ELEC 879 Wearable and loT Computing
ELEC 880 Machine Learning for Natural Language Processing

Other related courses offered by core ECE and cross-appointed faculty include ELEC 845 Autonomous Vehicle Control and Navigation, ELEC 474 Machine Vision, CISC 881 Machine Learning and Medical Image Processing, and EE 523 Integrated Navigation Systems (RMC)

MASc with a Field of Study in Artificial Intelligence Program Sheet

For information on admission requirements, application process, and funding, please see our Master of Applied Science page

Faculty

The core ECE faculty delivering the curriculum components of the MASc with the field of study in AI are:

Vector Faculty Affiliate

Xiaodan Zhu: machine learning, deep learning, and natural language processing
Ali Etemad: Machine learning, Internet of Things, data science
Michael Greenspan: Machine vision, image processing
Geoffrey Chan: Speech and signal processing, machine learning
Joshua Marshall: Intelligent robotics
Steven Blostein: Sensor networks, IoT, machine learning in wireless communications
Jianbing Ni: Machine learning security and cybersecurity
Il-Min Kim: Deep learning, reinforcement learning, IoT, mobile AI
Ahmad Afsahi: High-performance deep learning
Saeed Gazor: Machine learning, statistical image and signal processing
Keyvan Hashtrudi-Zaad: Autonomous driving

Karen Rudie: Discrete-event systems, intelligent agents
Ning Lu: Internet of Things, communication networks for autonomous vehicles
Shahram Yousefi: Machine learning in resource allocation, data storage, and telecom
John Cartledge: Machine learning, optical communication<
Ryan Grant: AI/ML at Extreme-scale, AI/ML applications in Smart Networks

Cross-appointed Faculty

Parvin Mousavi: AI in biomedical computing/engineering, School of Computing
Gabor Fichtinger: Applications of AI in computer-integrated surgery, School of Computing
Hossam Hassanein: Machine learning for communication networks, School of Computing
Aboelmagd Noureldin: AI and machine learning for multi-sensor positioning and navigation, RMC

Vector Scholarships in Artificial Intelligence

To attract top students in Ontario and from around the globe into a growing number of AI-related master’s programs, Vector’s merit-based entrance scholarships recognize promising AI talent applying to Ontario universities. Scholarships are valued at $17,500 for one year of full-time master’s study at an Ontario University. Prospective MASc students for the field of study in AI with first-class standing are encouraged to apply for Vector scholarship through Debra Fraser and Cheryl Wright. For eligibility and application components, please consult Vector Scholarship in Artificial Intelligence.

Career Opportunities

Our graduates have found careers:

As university professors, including at the University of Toronto, University of British Columbia, Carleton University, and Institut national de la recherche scientifique (INRS)
With high tech companies such as AMD, BlackBerry, Ciena, Cisco Systems, Google, Huawei, IBM, Intel, Infinera, Microsoft, Nokia
With startup companies
In service sectors such as financials, pension, actuarial, intellectual property

Interested in learning more about becoming a postdoctoral fellow? Learn more through Queen’s School of Graduate Studies and Postdoctoral Affairs.

Program Contacts

Graduate Program Assistant
Cheryl Wright
cheryl.wright@queensu.ca
613-533-6000 ext. 79307
Walter Light Hall, Room 416
Graduate Program Assistant
Debie Fraser
fraser.d@queensu.ca
613-533-2179
Walter Light Hall , Room 416A