AI Master’s Programs & Scholarships
Vector Scholarships in Artificial Intelligence (VSAI)
To recognize top students enrolled in AI-related master’s programs or pursuing an individualized study path that is demonstrably AI focused, Vector has launched the merit-based Vector Scholarships in Artificial Intelligence (VSAI). These scholarships are intended to assist universities in recruiting top tier students and deepening the applicant pools associated with AI-related master’s programs.
Vector Scholarships in Artificial Intelligence (VSAI)
To assist universities in recruiting top talent and deepening applicant pools, Vector has introduced the Vector Scholarships in Artificial Intelligence (VSAI). These merit-based awards recognize top candidates pursuing studies in a recognized AI-related master’s program or an individualized study path that is demonstrably AI-focused. Meritorious candidates must be nominated by their program; they cannot apply directly.
Vector Scholarships in Artificial Intelligence (VSAI) Details and Nomination Process
Please fill out the corresponding form and email the completed form to AImasters@vectorinstitute.ai (Please ensure that your email attachment size is under 20 MB.)
*If you are encountering issues sending your completed form to the above email, applications may also be submitted through the VSAI submission form here.
*Note that students cannot directly apply for the scholarship. Interested students should contact their program director to inquire about this scholarship program.
Deadline for nominations has now closed
For Recognized AI-Related Programs, please use Vector Scholarships in Artificial Intelligence (VSAI) Nomination Form A
To nominate candidates pursuing individualized AI-focused study plans who are in a program that is not listed as a recognized programs, please complete Vector Scholarships in Artificial Intelligence (VSAI) Nomination Form B
Frequently Asked Questions
Students must be nominated by their program, they cannot apply directly
Nominees must be new to program in September 2018 or be starting their AI- related program in the 2018-19 academic year (i.e. January 2019 or May 2019 start).
There is no restriction; however, as merit-based awards that are limited in number (up to 90 scholarships in total across the province), programs are expected to nominate its top candidates.
Only programs that have been recognized can use Form A (see list: https://vectorinstitute.ai/aieducation/; all others, including those “in progress’, must use Form B.
It is a requirement that a plan of study that is demonstrably AI-related be provided for each nominee ranked using Form B.
*Note that programs using Form B may have an earlier deadline for submission of materials to allow time for ranking.
First, seek advice from your institution. It is generally the case that the admission to program file (inclusive of all materials) may be used for the purpose of adjudicating meritbased funding for which the student can be considered. In some cases, it may be that the use of application materials is reserved solely for the purpose of admission to program, unless the student and their referees’ consent (programs would therefore seek consent in association with those nominated).
Several programs have introduced a formal AI specialization identifying those students pursuing formally designated AI-related study within a broader degree program; as such only Form A should be used to nominate top candidates.
The competition is intended to draw applicants to AI-related programs and aid universities to attract talent; therefore, the 2019-20 VSAI competition will be announced early in 2019 with the adjudication expected to be completed in spring. Further, with the information obtained through the 2018-19 competition and lessons learned, some modifications may be made.
Recognized AI Master’s Program
The AI master’s initiative aims to support the augmentation of a diverse and highly skilled AI talent pool aiding Ontario-based companies and labs in attracting and retaining newly formed talent. Ontario universities have been actively engaged in enhancing existing AI-related master’s programs and developing new purpose-built programs that have been recognized as delivering a curriculum that equips its graduates with the skills and competencies sought by industry.
Description: The University of Guelph’s Collaborative Specialization in Artificial Intelligence (AI) provides thesis-based Master’s students in Computer Science, Engineering, Mathematics and Statistics, and Bioinformatics with a diverse and comprehensive knowledge base in AI. Students will learn from a multidisciplinary team of faculty with expertise in fundamental and applied deep learning and machine learning, while conducting AI-related research guided by a faculty supervisor. Through a combination of online learning, lectures, team-based problem-solving and experiential learning opportunities, students will obtain broad expertise in machine learning and AI. They will gain essential skills in programming and algorithmic thinking, mathematical foundations and statistical analysis for AI, optimization, and data visualization. Additionally, graduates will have a deep understanding about the policy, regulatory and ethical issues related to AI.
By the end of this program, graduates will have comprehensive understanding of leading-edge AI techniques and will be able to apply this knowledge to solve real-world problems. Graduates will find employment opportunities in government and private industry, filling in-demand jobs such as machine learning research scientists, consultants, data scientists, and software engineers.
Program: Master of Management in Artificial Intelligence (Smith School of Business)
Description: Harnessing AI’s potential for competitive performance requires a new type of professional. One who not only understands the capacity of the science but has the expertise to apply it to organizational needs and strategies, and who can navigate the ethical, economic and societal implications. The Master of Management in Artificial Intelligence will provide:
- A strong understanding of the technical principles of AI and modern methods for data search and retrieval, and how to practically apply them to real world problems;
- Command of best practices in identifying opportunities for and applying AI;
- Expertise in the business practices required to effectively use AI and machine learning;
- Awareness and engagement in the ethical, economic, and societal impacts of computers exhibiting intelligent behavior;
- Enhancement of skills for communicating complex problems and AI solutions to wide audiences in an organization; and
- Training in creating and maintaining high performance work teams.
Description: The Queen’s School of Computing offers a Field of Study in AI which prepares students for AI-related work in leading technology firms, healthcare companies, automobile manufacturers, and research labs.
Students take five graduate courses and complete an AI-related MSc thesis over a period of 18 to 24 months. Upon graduation, students will have a solid background in core AI with a “Field of Study in AI” credential on their transcript.
The MSc thesis gives the student experience delving deep into a single AI problem in either core AI or an application of AI. The thesis typically provides training in project definition, substantial implementation, the use of real-world AI tools and packages, and technical writing.
Students can select from AI courses among deep learning, reinforcement learning, data mining, neural networks, nonlinear optimization, and pattern recognition. Students also take a research methods and AI-in-society course and may choose a non-AI course as part of the program.
Program: Master of Engineering (Electrical, Computer and Biomedical Engineering, AI)
Description: Ryerson University’s Department of Electrical, Computer and Biomedical Engineering Master of Engineering program (MEng) with an AI concentration will provide students with the essential training to be successful professionals in the field of AI. Students will be required to take four core courses: 1) Intelligent Systems, 2) Neural Networks, 3) Deep Learning, and 4) Advanced Data Engineering. Four interdisciplinary electives will also be required in application areas such as energy, sustainability, computer networks, and digital media—which will be studied through an AI lens. At the core of the program, the students will study ethical and societal implications of AI, covering topics such as bias, fairness and accountability. One of Ryerson’s strengths is combining theory with experiential learning; as such, a practicum will present students with the opportunity to solve a real-world problem using AI. 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 to solve some of industry and society’s greatest challenges.
Description: The applied artificial intelligence (AI) concentration within the existing Master of Computer Science program at uOttawa encompasses both the foundations and applications of machine learning. This unique program emphasizes the application of machine learning and AI to solve problems in areas such as health care, medicine, education, manufacturing and cyberspace.
Courses include machine learning and other AI-related topics such as deep learning, reinforcement learning, natural-language processing, computer vision, semantic web and intelligent physical systems. The program also includes a seminar, which centres on the ethical and societal impact of AI, while fostering community building and strengthening students’ communication skills.
Students will become experts in AI-related algorithms and methodologies, and will acquire knowledge to manage, assess and explore a variety of data across multiple application areas. They will further gain an understanding of the potential of bias in AI technologies, the ethics, benefits and dangers of AI, and the impact of AI on society. These are scarce competencies that are immediately useful and valued by industry, as well as academia.
Le programme en intelligence artificielle (IA) appliquée du programme de maîtrise en informatique de l’uOttawa couvre tant les aspects fondamentaux que les applications liées à l’apprentissage automatique. Ce programme unique met l’accent sur la résolution de problèmes concrets liés aux soins de la santé, la médecine, l’éducation, l’industrie manufacturière et le cyberespace.
Les cours portent sur l’apprentissage profond, l’apprentissage par renforcement, le traitement automatique des langues, la vision artificielle, le web sémantique et les systèmes intelligents. Ce programme comprend aussi des séminaires portants sur les aspects éthiques et sociaux de l’IA.
Les étudiants poursuivant la concentration en IA deviendront des experts en algorithmes et méthodologies d’apprentissage automatique et posséderont les connaissances nécessaires pour gérer, évaluer et explorer une variété de données dans de multiples domaines d’application. Ils acquerront une compréhension du risque de biais dans l’apprentissage machine et d’autres technologies d’IA, ainsi que l’éthique, les avantages et les dangers de l’IA, et l’impact de l’IA sur la société. Il s’agit de compétences rares qui sont très appréciées tant par l’industrie que par le milieu universitaire
Program: Master of Management Analytics
Description: The Rotman Master of Management Analytics (MMA) is a nine-month, full-time program running from August to April. The program is focused on teaching the applications of advanced analytics, AI, and machine learning to provide data-driven insights on complex managerial problems. Balancing rigorous theoretical knowledge with experiential learning opportunities, the program is structured to first develop tools and techniques, and then focus on the holistic application of analytics and AI to problems arising in various areas of management. Hands-on skills, including coding and knowledge of advanced analytic tools are emphasized, as are effective communication and presentation skills. Two unique features of the program are the Analytics Colloquia, consisting of modules ranging from Ethics in AI to Applications of Quantum Computing, and the Management Analytics Practicum, where students work on a real-world business problem over the course of a 9-month project. The practicum projects in 2018/19 reflect broad-based interest and come from a wide cross-section of industries including Banking, Pharmaceutical, Telecommunications, Insurance, Retail, and Services.
Description: The Data Science specializations that are offered in the Master of Mathematics (M.Math.) in Computer Science and the M.Math. in Statistics have a common structure but a different emphasis depending on the student’s academic home. As a joint venture between Statistics and Computer Science, students are taught by experts from both academic units and thus gain breadth and depth in areas that are essential for Data Science. The core and elective courses contain a mix of methodological and analytical content, blended with exposure to practical applications through the use of case studies and extensive projects. The main learning outcomes for this program are to gain computational, statistical, and mathematical foundations relevant for Data Science; become proficient in working with data, and understand how to use appropriate machine learning methodologies for inference and prediction. Graduates of this program will be ready to join an industry sector where there is a high demand for their skills. Also, with their strong core training they will be able to adapt easily to changes and new demands from industry.
Description: York University’s Master of Science in Computer Science program with specialization in AI has been strategically designed to provide its graduates with a solid grasp of the computational framework underlying AI methodologies and with hands-on experience on how they are applied to diverse computing problems. The core coursework focuses on fundamentals of AI, machine learning theory, data mining and ethics of AI. A broad range of elective courses that focus on applications of AI in areas such as computer vision, robotics, neural networks & deep learning, natural language processing and data mining & analytics provide students with the ability to tailor their education according to career goals. Under the supervision of a faculty member with expertise in AI, the completion of a research project gives graduates a competitive advantage by leveraging York University’s partnerships to gain coveted industry experience. With this knowledge, graduates are positioned to successfully deploy AI methodologies across many sectors
Seeking Program Recognition