Practical applications of AI have exploded in recent years with a resurgence of research in machine learning and, especially, in deep learning. Real-world applications include medical diagnosis, text and voice analysis for big data applications, computer vision, and self-driving cars.
There is a growing demand for graduates with an AI background from the leading technology firms, healthcare companies, automobile manufacturers, and research labs. The creation of a "Field of Study in Artificial Intelligence" will help to distinguish Computing graduates and increase their competitiveness for these highly skilled positions. The School of Computing has substantial expertise in this Field, with core faculty members in AI covering diverse disciplines.
- Dorothea Blostein: biomechanics; adaptive tensegrity
- Steven Ding: data mining; machine learning; security
- Qingling Duan: machine learning; biomedical computing; bioinformatics
- Randy Ellis: medical data analysis
- Sidney Givigi: machine learning applied to autonomous vehicles; especially reinforcement learning and deep learning
- Ting Hu: evolutionary computing; machine learning; complex networks; bioinformatics
- Parvin Mousavi: machine learning in computer assisted diagnosis and interventions; image-guided interventions; medical image computing; computational biology; bioinformatics; systems biology
- Christian Muise: automated planning; model understanding, learning, and acquisition; goal-oriented dialogue systems
- Amber Simpson: machine learning; medical image analysis; computer aided surgery
- David Skillicorn: adversarial knowledge discovery
- Sameh Sorour: cyber-physical and autonomous systems; intelligent vehicles and transportation systems; mobile edge learning; Internet of Things (IoT); edge computing and networking; network coding
- Catherine Stinson: bias and explanation in machine learning; computational psychiatry; embodied intelligence; community-led design
- Yuan Tian: deep learning; software engineering; machine learning, recommender systems
- Farhana Zulkernine: big and streaming data management and analytics; deep learning and Decision Support Systems (DSS); cognitive computing; cloud and services computing
The requirements of the Field are that the student take three courses in AI, complete an AI-related thesis and complete the other requirements of the degree program. Graduates that complete the requirements will receive the "Field of Artificial Intelligence" designation on their transcript.
For the MSc (research-based only), the Field of Study in Artificial Intelligence will require that student:
- take three courses from the list of AI courses, including at least one of CISC 856, CISC 867 and/or CISC 874;
- take two more courses as required in the MSc program; and
- complete an AI-related thesis.
For the PhD, the Field of Study in Artificial Intelligence will require that student:
- satisfy three tokens that show graduate course-equivalent experience in three of the AI courses listed, including at least one of CISC 856, CISC 867 and/or CISC 874;
- satisfy seven more tokens as required in the PhD program; and
- complete an AI-related thesis.
- CISC 843*/3.0: Mining Software Repositories
- CISC 851*/3.0: Evolutionary Computing
- CISC 855*/3.0: Nonlinear Optimization
- CISC 856*/3.0: Reinforcement Learning
- CISC 859*/3.0: Pattern Recognition
- CISC 867*/3.0: Deep Learning
- CISC 873*/3.0: Data Mining
- CISC 874*/3.0: Neural and Cognitive Computing
Vector Scholarships in Artificial Intelligence (VSAI)
The Vector Scholarship in Artificial Intelligence is valued at $17,500 for one year of full-time study at an Ontario University. These merit-based entrance awards recognize exceptional candidates pursuing a master’s program, such as the Artificial Intelligence Field of Study, that is recognized by the Vector Institute or who are following an individualized study path in a range of disciplines that is demonstrably AI-focused. Both domestic and international students with first class standing (minimum A- in their last two years of full-time study) are eligible for consideration. Nominations for the 2020/21 academic year open on January 4, 2021.
Students persuing the AI field of study are encouraged to apply for the scholarship through Debby Robertson. Applications received by February 24 will be reviewed and given feedback before the final March 10th deadline.
- February 24, 2021 - Deadline to get feedback (first come, first served basis)
- March 10, 2021 - Final Deadline
Scholarship applications must be submitted through the program(s) the prospective student has applied to and include the following components:
- copies of all official transcripts;
- two references using the referee form (at least one of two references must be academic);
- Note that the referee forms are not the same forms submitted when you applied to your program. Forms should be submitted by the referee and not the applicant.
- an up-to-date one to two-page CV; and
- a 250-word statement outlining your reason for pursuing a master's in AI, relevant AI-related experience, and career aspirations;
- if not enrolled in a Vector-recognized program, an approved study plan including a course list and description of thesis/capstone project; and
- a self-identification questionnaire.