AI MASTER’S: BUILDING ONTARIO’S AI ECOSYSTEM
The Vector Institute is building an AI workforce to drive the competitiveness of Ontario-based companies and labs and to strengthen Ontario’s position as a global destination in AI. The Vector Scholarship in Artificial Intelligence, valued at $17,500, helps to attract the best and brightest students to study in AI-related master’s programs in Ontario. There are currently over 20 master’s programs in Ontario recognized by the Vector Institute for equipping graduates with AI skills and competencies sought by industry. These include new and expanded programs in core technical and complementary areas such as business and health. By providing an increasing number of students and graduates with meaningful professional development and linking them to compelling internship and career opportunities through the Vector Digital Talent Hub, the Vector Institute is positioning Ontario to lead the way in AI adoption.
The Vector Scholarship in Artificial Intelligence supports the recruitment of top students to AI-related master’s programs in Ontario. 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 recognized by the Vector Institute or who are following an individualized study path that is demonstrably AI-focused. Students in both core technical programs and complementary fields such as business and health are eligible for nomination.
The nomination period for the Vector Scholarships in Artificial Intelligence is now open!
Referee forms can be found by clicking here.
The deadline for submitting nominations is April 3, 2020 at 4 pm EST.
Scholarship recipients by institution:
Spotlight on scholarship recipients:
2018-19 Vector Scholarship in AI Recipient
University of Waterloo, MASc, Systems Design Engineering
Previous Degree: B.Eng, Biomedical Engineering (University of Guelph)
How my research will have an impact: My research interests lie in the social impact of artificial intelligence with projects on auditing training datasets for representational bias, benchmark datasets for evaluation bias and on fairness and privacy implications of face recognition.
2018-19 Vector Scholarship in AI Recipient
University of Toronto, MSc, Computer Science
Previous Degree: BSc, Computer Science (Peking University)
What are you working on now? In 2019, I was published in three different conferences: CVPR on interactive object segmentation, ICLR on analyzing representations in language modelling and NeurIPS on differentiable rendering and trying to learn 3D shapes from 2D images. My goal is to get into all the computer vision conferences, including ECCV and ICCV in the next year. I also work part-time as an intern at NVIDIA, where I get to learn more about graphics, 3D and AI in that domain.
2018-19 Vector Scholarship in AI Recipient
University of Ottawa, MSc in Computer Science, Applied AI Concentration
Previous Degree: BSc, Computer Science (Alexandru Ioan Cuza University of Iasi)
How my research will have an impact: My thesis will be focused on the explainability of NLP algorithms. We have to keep an eye on the applicability of research in industry and work to eliminate the gap between pure research and specific applications. My thesis has the potential to touch so many industry applications including the medical domain, when AI agents are supporting the decision making process for specialists and doctors.
2019-20 Vector Scholarship in AI Recipient
University of Waterloo, MASc in Systems Design Engineering
Previous Degree: BASc in Electrical, Electronics and Communications Engineering (Amirkabir University of Technology, Tehran Polytechnic)
What are you working on now? I’m currently working on a project to analyze histopathology images by applying U-Net, a convolutional neural network for biomedical applications, to segment tissue and remove background information. We will also compare this tool against other image processing tools as well to find out which method is more accurate or faster. So far, no one has used U-net for segmenting tissue. This application will have implications for cancer detection in the future, which is so inspirational and I am excited about.
2019-20 Vector Scholarship in AI Recipient
Queen’s University (Smith), Master of Management in Artificial Intelligence
Previous Degrees: Doctor of Medicine (University of Calgary); Master of Public Health (Queen’s University)
What’s next? As a physician, I’m lucky to work with colleagues and patients who continue to strive for better outcomes. We are working on NLP and Image Detection solutions currently to solve health system demand problems on a global scale. In the future, I hope to continue to combine my experience in healthcare and AI to work with like-minded people to solve problems and develop great solutions.
Why study in Ontario
Canada has been at the forefront of AI research for several decades and researchers, such as Vector’s Chief Scientific Advisor, Dr. Geoffrey Hinton, have led the way in ground-breaking work in deep learning and neural networks, contributing to significant advances in AI.
Ontario is home to 22 public universities, many of which are delivering cutting-edge curricula in AI across a range of disciplines, from health to computer science to business and engineering. Learn more about Vector recognized core-technical and complementary programs.
Ontario offers a safe and inclusive environment to study. Ontario’s population is incredibly diverse, welcoming people from around the globe. Half of the population of Toronto, Ontario’s largest city, is comprised of people born outside of Canada, and Toronto is one of the most multicultural cities in the world.
Many students who pursue their master’s degree in Canada take advantage of the ability to work part-time while studying full-time as well as favourable post-graduate immigration pathways. Students pursuing a two-year master’s degree may be eligible for a post-graduate work permit of up to three years. Learn more at Immigration, Refugees and Citizenship Canada.
Scholarship Eligibility & Nomination Process
As students cannot apply directly to the Vector Institute for the scholarship, they are encouraged to work with the Ontario university to which they have applied for full-time master’s study. Individual programs may have specific internal deadlines related to the scholarship for applicants they will be nominating. Scholarship nominations open January 6, 2020 , and all scholarship nomination packages must be submitted by Ontario universities to the Vector Institute by April 3, 2020.
To be eligible for consideration for the scholarship, prospective students must:
- Be applying for entrance into a full-time master’s program in an AI-related field at an Ontario university for the 2020-21 academic year. You may be applying to a Vector- recognized program or be pursuing an individualized study plan that is demonstrably AI-focused at an Ontario university.
- Hold first-class standing (minimum of A- or equivalent) in the last two years of study (full-time equivalent) from a recognized university. It is expected that the prospective student’s transcript will reflect preparedness for the proposed master’s study. Consideration will be given to candidates with upper second class standing (B+) and relevant work experience or where extenuating circumstances apply if rationale for consideration is included.
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 (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. Referee forms are available by clicking here;
- 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.
If you receive a Vector Scholarship in Artificial Intelligence for 2020-21, you must be registered full-time at the Ontario university by which you were nominated to receive the award. Scholarship funds will be provided directly to the university to disperse to you. Please note that the award is non-transferable.
FAQ for Students
Programs can nominate eligible candidates who will or who have received an offer of admission. Candidates cannot apply directly for a Vector Scholarship in Artificial Intelligence. However, to support the nomination, candidates must supply a short statement, CV and solicit two referee reports to be sent directly to the program(s) to which the candidate is applying. Prospective students should alert the program of their interest in being nominated for a Vector Scholarship in Artificial Intelligence.
No, these are entrance scholarships for AI-related master’s students who will register and begin their studies in the 2020-21 academic year; others are not eligible.
No, the scholarships are restricted to AI master’s students studying at an Ontario university.
No, the scholarship is awarded to the candidate for study in the master’s program that submitted the nomination; it cannot be transferred to another program of study or to a similar program at a different university.
Yes, you can hold a Vector Scholarship in Artificial Intelligence and an OGS or CGS-M simultaneously.
Usually, the Vector Scholarship in Artificial Intelligence must be taken up in accordance with the start date indicated by the program which nominated you. However, in extenuating circumstances (e.g. unanticipated: medical issues, family responsibilities, or visa delays), deferral may be granted with relevant documentation.
The combined support of the Province of Ontario and a commitment from Ontario’s universities to recruit top talent and increase the number of graduates from AI-related master’s programs are contributing to a robust AI ecosystem. The merit-based Vector Scholarships in Artificial Intelligence assist universities in attracting excellent students from Ontario and around the world and deepening the pool of applicants. In addition, the scholarship supports students in their pursuit of AI-related studies while building community among scholars.
Up to 115 Vector Scholarships in Artificial Intelligence will be awarded in 2020-21, each valued at $17,500 for one year (prorated for programs less than 12 months in duration).
Nomination Process & Eligibility Requirements
Ontario university programs may nominate their top applicants to the Vector Institute for scholarship consideration. For Vector recognized master’s programs, please submit your nominations using Form A. For all other master’s programs, please use Form B. The Vector Institute does not accept applications directly from prospective students.
Programs are required to rank their nominations and include supporting documentation on each candidate nominated by the program, including the following:
- copies of all official transcripts with the corresponding grading system (both undergraduate and, if applicable, graduate transcripts);
- two referee forms, which will become available as of January 6, 2020;
- an up-to-date one to two- page CV; and
- a 250-word statement from the candidate outlining his/her 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.
For programs that are not currently recognized, each nomination must include a description of the candidate’s AI-related study plan (e.g., course numbers & titles, capstone/project description) signed by the nominee and the Graduate Curriculum Chair or faculty advisor.
To be eligible for consideration for a scholarship, candidates must have applied for entrance to a master’s program in an AI-related field at an Ontario university for the 2020-21 academic year and meet the Vector scholarship eligibility criteria. Specifically, candidates must hold first-class standing (minimum of A- or equivalent) in their last two-years of study (full time equivalent) from a recognized university. Consideration will be given to candidates with upper second class standing (B+) and relevant work experience or where extenuating circumstances apply (include a rationale for consideration).
FAQ for Program Administrators & Faculty
The scholarship is intended to attract strong applicants and students to your program; as such, making applicants and prospective applicants aware of these awards is important. Easy to share marketing collateral on the scholarship has been distributed to programs. If your program would like this material, kindly email email@example.com.
No, programs can nominate eligible candidates who are eligible for admission or have received an offer of admission. Candidates cannot apply directly for a Vector Scholarship in Artificial Intelligence. However, to support the nomination, candidates must supply a short statement, a 1 to 2 page CV, and solicit two referee reports to be sent directly to the program(s) in which the candidate has applied. Prospective students may alert the program of their interest in being nominated for a Vector Scholarship in AI. In addition, the program can solicit top applicants to complete the requisite documentation for the scholarship application.
No, these are entrance scholarship for AI-related master’s students who will register and begin their studies in the 2020-21 academic year; others are not eligible.
Yes, providing they meet all the eligibility requirements and are highly qualified to merit nomination by the intended program of study.
Programs are responsible for nominating incoming AI master’s students. This involves:
- Ensuring that eligible applicants to your program have been instructed to provide their 250-word statement, a 1 to 2 page CV, and seek references from two referees using the referee report form which will be made available January 6, 2020.
- Providing the prospective student with the name and email address of your program contact and the deadline date by which the contact must receive the statement, CV (if not included in your application) and referee reports (as per #1);
- Ensure that the deadline for submission of material is far enough in advance of Vector’s submission deadline of April 3, 2020 to allow for the ranking of nominees and the collation of the nomination package; and
- Complete and submit the appropriate nomination forms (available starting January 6, 2020) along with the nomination package. Full instructions on the nomination package will be made available on January 6, 2020.
No, the scholarship is awarded to the candidate for study in the master’s program that submitted the nomination; it cannot be transferred to another program of study in either the same university or a different university. In such cases, the scholarship reverts to Vector for possible re-allocation.
Yes, a Vector Scholarship recipient may hold an OGS or a CGS-M at the same time.
Usually, the Vector Scholarship in Artificial Intelligence must be taken up in accordance with the start date specified on the nomination form. However, in extenuating circumstances (e.g. unanticipated: medical issues, family responsibilities, or visa delays), deferral may be granted with relevant documentation.
Funds are transferred to the universities and paid out through the Faculty/School of Graduate Studies student awards system in instalments. All funds go directly to and in full to the recipients, providing they maintain full-time enrolment and are in good standing.
Why work in Ontario
Leading global companies and promising start-ups recognize that Canada is among the best places to collaborate with and hire AI talent. Since the launch of the Pan-Canadian AI strategy in 2017, the Vector Institute has been among a series of catalysts for over $1 billion of announced AI and tech-related investments, which will result in the creation of 25,000 jobs across Canada.(i)
Did you know that:
- Tech talent comprises a high percentage of total employment in markets such as Ottawa (9.9% of market) and Toronto (8.3% of market)(ii), attracting leading companies to these regions, fueling entrepreneurial ventures and accelerating innovation.
- Between 2012-2017, more tech jobs were created in Toronto than in San Francisco, Seattle and Washington combined. In fact, Toronto ranks among the top 3 tech talent markets in North America (as measured by cost and quality) with an excess of 57,000 tech jobs added over tech degrees granted between 2012- 2018, topping the tech “brain gain” in North America.(ii)
- In Canada, AI venture capital funding has increased for the second consecutive year, with companies raising $548 million in 2018, a 51 per cent increase year-over-year.(iii)
- Supply for skills in areas such as machine learning is not keeping pace with demand(ii) creating dynamic opportunities across a variety of industries for AI master’s graduates.
(i) Economic analysis prepared by Stoke Economics for the Vector Institute, December 12, 2018
(ii) CBRE report: “Scoring Tech Talent in North America 2019”
(iii) PwC Canada CB Insights Money Tree Canada Report Q4 & Full-Year 2018
Vector Digital Talent Hub
Students and alumni* have access to Vector’s Digital Talent Hub, a platform that bridges the offline component of Vector’s programming with the online recruitment processes of employers. Users can browse and apply to AI job postings, set up automated job alerts, and upload their profile details for employers to contact them regarding career opportunities. Students and alumni* are welcome to Create a Profile.
Career Supports for Students
Vector-affiliated students and alumni* enjoy access to career development and networking activities. These opportunities enable students to explore various career paths in AI, better understand the opportunities and challenges of AI adoption across different sectors, and meet with potential employers. Sign up to learn about events and more!
*including students and graduates from Vector-recognized programs, recipients of the Vector Scholarship in Artificial Intelligence, as well as researchers affiliated with Vector Faculty Members and Faculty Affiliates.
Networking among students in the Kitchener-Waterloo/Guelph area after the Careers in AI panel held at Communitech in Kitchener (July 2019).
Careers in AI Panel in Ottawa (September 2019, University of Ottawa): Experienced industry representatives working in AI share about their career trajectories, opportunities and challenges in today’s AI industry. Left to right: Melissa Judd (Vector Institute), Maria Pospelova (Interset), Mike Cloutier (Accenture), Robin Grosset (Mindbridge Analytics Inc.), Nevena Francetic (Shopify Inc.).
Vector AI Master’s Summit & Career Fair (September 2019, Toronto): At this inaugural event, over 300 master’s students from across Ontario had the opportunity to meet with 18 of Vector’s sponsors to learn more about career opportunities in AI and make meaningful connections. Photographer: Calyssa Lorraine
Vector AI Master’s Summit & Career Fair (September 2019, Toronto): Students were given a passport that encouraged them to network and build relationships with other graduate students from over 30 programs across the province. Photographer: Calyssa Lorraine
The Vector Institute has been tasked with supporting Ontario’s growing AI eco-system including the goal of accelerating the number of artificial intelligence (AI)-related master’s graduates. To achieve this goal, universities with expertise in AI-related areas are invited to expand or enhance relevant existing master’s programs or create new AI-related programs to:
- Meet the essential requirements articulated by Vector for core technical AI and complementary AI-related fields; and
- Prepare highly qualified graduates who demonstrate area-specific advanced knowledge, skills and competencies sought by the AI-sector to build a highly skilled workforce and support economic growth and productivity.
Programs must prepare graduates to meet all essential requirements as well as advanced AI field specific learning outcomes. The Vector Institute Guidance Document for AI-Related Master’s programs details both the process and requirements for program recognition. Programs are invited to complete the Program Recognition Submission form to be considered for recognition. While submissions for Vector recognition can be made at any time, the following are the scheduled meeting dates of the Program Recognition Panel for the 2019-20 academic year:
- September 18, 2019
- November 20, 2019
- January 22, 2020
- March 18, 2020
- May 20, 2020
Benefits of Program Recognition
Programs recognized by Vector will be identified as academic partners and listed on Vector’s website as part of the AI Master’s initiative. Programs may highlight that they have been recognized by the Vector Institute as delivering a curriculum that equips graduates with the skills and competencies sought by industry.
Students enrolled in recognized programs can take advantage of:
- Career development & supports such as the Vector AI Master’s Summit & Career Fair, where students meet with like-minded peers from across Ontario and interact with potential employers that are building AI teams and adopting the technology across their organizations.
- Opportunities to connect with Vector industry sponsors, who are at the forefront of AI in Canada through education and networking events hosted by the Vector Institute, such as the Careers in AI Panel.
- Access to internships and full-time roles through the Vector AI Digital Talent Hub featuring AI-related opportunities.
- Exposure to the latest AI research from around the globe through events such as:
- The Fields Institute Machine Learning Advances and Applications Seminar series, which strengthens the machine learning community in Ontario by bringing together researchers from both academia and industry to discuss advanced topics in machine learning.
- Health AI Rounds, a series featuring leading international health researchers that connects the health research and AI communities and seeds new collaborations. View previous Rounds discussions at: https://vectorinstitute.ai/research/#talks.
- Opportunities to engage with other students and alumni in AI master’s programs across Ontario, both on and off-line.
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.
Master of Science (Computer Science – AI)
Program description to be made available soon
Ontario Tech University offers a specialized field within its Master of IT Security program with a focus on Applications of Artificial Intelligence in IT Security (MITS-AIS). This professional stream is the first of its kind in Canada and combines a deep knowledge of IT Security with hands-on knowledge of artificial intelligence systems and machine learning, and provides students with a comprehensive understanding of the applications of this technology. Most courses in the MITS and MITS-AIS program include hands-on and laboratory assignments that simulate a variety of computer networking, security attacks and defence scenarios.
Students must complete 30 credits, including six mandatory and two elective courses in AI and IT Security; a seminar course; and the option of either a Capstone project to conduct research on a topic related to AI and security under the supervision of a faculty member, or an industry internship; subject to availability and program director’s approval.
Graduates of this program can seek employment in the growing artificial intelligence industry, public and private organizations, as well as IT security firms.
University of Ottawa / Université d’Ottawa
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, that 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. Students 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.
Master of Applied Science (MASc) in Electrical and Computer Engineering and Master of Engineering (M.Eng) in Electrical and Computer Engineering — Applied Artificial Intelligence Concentration
The objective of this program is to graduate highly qualified electrical and computer engineers who not only are highly skilled in AI theory, but are also equipped with the desired skills to apply their knowledge across multiple domains. To this end, it covers both the fundamental knowledge in machine learning and its applications to the problems in electrical and computer engineering. Furthermore, this program leverages the application of machine learning and AI to innovate in areas such as communications, devices, utilities, transportation, digital health, cybersecurity and manufacturing.
MASc students complete a thesis, while M.Eng. students complete a project. Both are eligible for and encouraged to do an industry internship. The course on social implications and ethics equips students with effective oral and written communications skills to communicate with generalist audiences and clients. The program ensures students are able to develop and present AI-based solutions, and apply AI-based methodologies to solve practical problems in electrical and computer engineering. These include topics such as robotics, digital health, cyber-physical systems, wired/wireless communications, cyber-security and multimedia systems. As well, the students work with professionals from other disciplines to integrate AI-based solutions into services and products in other domains.
Maîtrise ès sciences appliquées Génie électrique et génie informatique (M.Sc.A), Maîtrise en ingénierie Génie électrique et génie informatique (M.Ing) – Concentration en intelligence artificielle appliquée
L’objectif de ce programme est de former des ingénieurs électriciens et informaticiens hautement qualifiés en théorie de l’intelligence artificielle (IA) et dotés des compétences requises pour appliquer leurs connaissances à plusieurs domaines. À cette fin, ce programme couvre à la fois les connaissances fondamentales en apprentissage automatique et ses applications aux problèmes d’ingénierie électrique et informatique. En outre, ce programme exploite l’application de l’apprentissage automatique et de l’IA pour innover dans des domaines tels que les communications, les dispositifs, les services publics, les transports, la santé numérique, la cybersécurité et la fabrication.
Les étudiants à la M.Sc.A. doivent compléter une thèse tandis que ceux à la M.Ing. doivent compléter un projet. Tous les étudiants peuvent s’inscrire à un stage en industrie, une option fortement encouragée. Le cours sur les implications sociales et l’éthique dans la conception, l’IA et la robotique fournit aux étudiants des expériences de communication orale et écrite, les préparant aux scommunication auprès de clients et du grand public. Le programme procure aux étudiants les habiletés et connaissances nécessaires au développement et à la présentation de solutions fondées sur l’IA pour des domaines du génie électrique et informatique tels que la robotique, les services de santé numériques, les systèmes cyber-physiques, les communications avec et sans fil, la cybersécurité et les systèmes multimédias. De plus, les étudiants travaillent avec des professionnels d’autres disciplines pour intégrer des solutions fondées sur l’IA à des services et produits d’autres domaines.
Queen’s University (Smith School of Business)
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.
The Master of Management Analytics – Toronto marries training in core data analytics concepts and tools, with outstanding education in business strategy and management. It includes extensive review of the fundamental mathematical and statistical theories and methods that underlie modern analytics, but with a practitioner focus. The program features some of the best management educators in the world, plus industry specialists and practitioners who bring their daily experience and insights to the discussion. Students will be exposed to a variety of tools and programming languages including R, Python, Tableau, Hadoop, and Spark, and technical courses are taught by experts and range from importing data and data visualization to machine learning, deep learning, and more. Take this 12-month program while working, with classes based at Smith Toronto, a dedicated learning facility in downtown Toronto.
The Queen’s School of Computing offers a Field of Study in AI that 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.
The Department of Electrical and Computer Engineering at Queen’s offers a unique Master of Applied Science program with a Field of Study in Artificial Intelligence (AI) that provides the 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 acquire advanced research and technical knowledge in AI-related fields and will have deep understanding about the ethical and societal implications of AI.
The Department has substantial expertise in AI-related methodologies and application areas. 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. A broad range of courses allow students to tailor their education according to career goals. The program, with its mixture of AI-related methodologies and application areas, prepares highly-qualified graduates with area-specific knowledge, skills, and competences highly sought by the public and private AI sector.
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.
This unique one-year Master of Science (MSc) degree program enables students to develop interdisciplinary skills and gain a deep understanding of technical and applied knowledge in data science and analytics. Graduates are highly trained, qualified data scientists who can pursue careers in industry, government or research. Core technical skills such as math, statistics, operations research, programming, machine learning and domain knowledge are the core competencies that are in greatest demand in the marketplace. The program is designed to meet the requirements of the marketplace. Data science is an interdisciplinary field that combines expertise from different technical and expert domains. The program is designed with the contributions of four different faculties to reflect the interdisciplinary nature of the field. The curriculum consists of six courses and a mandatory applied advanced project.
Students learn how data science and analytics can help to improve decisions throughout an organization’s value chain, understand how recommendations lead to tangible actions through prescriptive analytics techniques, gain a good understanding of methods used in building such data-driven models, and acquire hands-on experience with machine learning algorithms that are widely used in practice.
University of Toronto (Rotman School of Management)
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 nine-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.
University of Toronto (Institute of Health Policy, Management and Evaluation)
The Master of Health Informatics (MHI) is a professional program enabling graduates to harness the potential of data and knowledge for the better management of health and care. Set within University of Toronto’s Dalla Lana School of Public Health, and the Institute of Health Policy, Management and Evaluation, MHI exists within a vibrant, interdisciplinary culture. Taught by health sector leaders, MHI focuses on upstream development of the healthcare complement in areas of policy, management and evaluation to prepare students to lead AI-based change. By design, each cohort reflects healthcare’s many stakeholders, and team-based learning draws together students from clinical, analytic, business, project management and technological backgrounds. MHI is a 16-month full-time program that includes a 4-month applied professional practicum and an AI-based capstone project. The Executive MHI is a modular stream enabling students to balance full-time employment and study. MHI graduates bridge complex social, cultural, political and technological settings in healthcare. With competencies in responsible innovation and knowledge-based implementation, graduates are ready to bring judgement and order to the process of assimilating AI and related knowledge into healthcare.
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.
Students in the Master of Data Science and Artificial Intelligence at Waterloo receive an in-depth education in the theory and practice of machine learning and computational analysis of massive data sets plus industrial experience. Offered jointly by three departments (Computer Science, Statistics and Actuarial Science, and Combinatorics & Optimization), available courses cover robust and efficient storage systems of massive data, effective statistical data exploration, and algorithms for discovering hidden structures. The program typically requires three terms on campus (to complete nine courses) and one term in an industrial co-op placement, for a total of 16 months. Waterloo’s mature and extensive co-operative education system and industrial ties give students opportunities to work at top data science and AI companies in North America. Entering students should have a bachelor’s degree in computer science, statistics, data science, or a related mathematical field. Background courses are available during the first term for students missing an aspect of the prerequisites.
Students in the Master of Mathematics in Data Science program at Waterloo work at the forefront of modern research in topics related to data science, encompassing statistics, computer science, and optimization. Degree requirements include the completion of four graduate courses and the writing of a Master’s thesis under the supervision of a leading expert from one of three departments (Computer Science, Statistics and Actuarial Science, and Combinatorics & Optimization) that participate in the program. The program requires 4-6 terms, i.e., up to two calendar years. The course requirements include data exploration, distributed computing with data, machine learning and optimization, and a fourth elective course. Students in this program receive financial support composed of research and teaching assistantships and scholarships. Entering students should have a bachelor’s degree in computer science, statistics, data science, or a related mathematical field. Background courses (not counting toward the degree) are available for students missing an aspect of the prerequisites.
Western’s Master of Data Analytics in Artificial Intelligence is a one-year professional science master’s program designed to produce technical data analytics professionals skilled in artificial intelligence, machine and deep learning, big data management and analysis infrastructure for unstructured data as well as other foundational areas of analytics, including visualization, databases, data carpentry and statistical modelling and inference, who are also effective communicators with complementary business skills and teamwork experience. The fall and winter curriculum consist of courses that focus on fundamental data analytics and AI-specific methods and their applications, along with a seminar series in the ethics and societal implications of AI. These courses are commonly taught by faculty who develop and apply data analytics and data science methods in their research. A career development seminar also runs over the fall and winter terms, providing students with the skills to successfully chart an effective course for their career. The summer term consists of an Experiential Learning Opportunity where students gain valuable practical experience using the analytics skills they have developed in a workplace environment.
Western University’s departments of Computer Science (CS) and Electrical and Computer Engineering (ECE) are jointly offering a collaborative specialization artificial intelligence (CSAI) concentration in existing Master programs in CS and ECE (i.e., MSc, MESc and MEng). CSAI enhances the education and research of a graduate student by adding a module to their program of study. The concentration is designed so that graduates have a solid foundation in artificial intelligence methodologies, enabling technologies that include big data computational platforms, data management systems, and GPU programming as well as social and ethical challenges/limitations/advantages of AI. A student in the program must demonstrate their mastery of artificial intelligence with an AI-related thesis, project or internship. The CSAI program, with its mixture of theoretical and hands-on training will provide graduate students with the quality experience necessary to prepare them for careers in high-tech Canadian industries.
The concentration in Artificial Intelligence is part of the thesis-based program of the Master of Science in Computer Science at the University of Windsor, and has a curriculum that includes a variety of AI and AI-related courses in specialized areas of computing technology, applications, and theory. The program introduces students to advanced topics in AI and AI-related subjects and applications, as well as to new AI technologies that are in high demand for computer professionals in the IT industry. The topics included in the AI stream cover the fundamental concepts of AI, concepts related to AI, as well as important applications of AI. Students graduating from the AI stream will not only be able to make contributions in the AI software industry as other non-streamed students, but also be ready for the new challenges to help Ontario’s growing AI ecosystem or to help advance AI and its related fields in their PhD studies. By the end of this program, graduates will have a 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 scientists, consultants, data scientists, and software engineers.
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 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.
The Master of Business Analytics (MBAN) offered by the Schulich School of Business is a professional degree program designed to provide students with the breadth and depth of knowledge to be successful in a wide range of careers in areas such as banking, insurance, marketing, consulting, supply chain management, healthcare, and technology. What makes the Schulich MBAN program unique is the combination of academic rigor and valuable practical experiences, made possible by a strong set of collaborative relationships with industry. World-class faculty, an advisory board composed of senior leaders from successful organizations (e.g., TD Bank, BMO, CIBC, Deloitte, TELUS, SAS, Rogers, Accenture, PWC, etc.), and project partnerships with dozens of client companies ensure that the program has remained at the cutting edge of the theory and practice of Artificial Intelligence. By the end of the program, graduates are able to apply business analytics and AI-supported techniques to generate concrete and actionable business solutions.
Artificial Intelligence (AI) is undergoing a landmark evolution, transforming the private and public sectors. As organizations adopt and invest in AI technology, a new style of management is needed – one that pairs a leader’s vision with a scientist’s mastery over a growing body of specialized knowledge. The 12-month Master of Management in Artificial Intelligence (MMAI) is designed to meet the growing need for talented professionals with the skills and advanced applied knowledge to develop, evaluate, refine and implement AI-related applications and technologies. The immersive curriculum offers a technical foundation in natural language processing, computational methods and modeling, paired with core business skills. Students will explore a critically evolving ethical landscape as they confront moral topics in AI, such as algorithmic bias, data privacy and intelligent agent autonomy. The capstone Artificial Intelligence Consulting Project (AICP) provides students with an opportunity to solve a significant business problem by designing an AI-centered approach. Working in the Deloitte Cognitive Analytics and Visualization Lab student teams will deliver a solution to a client organization, interacting with industry managers, technicians, suppliers and other stakeholders.
The Vector Scholarships in Artificial Intelligence, together with internships and networking programs, are a core component of the Vector Institute’s RAISE initiative, supported by the Province of Ontario, to develop and connect Ontario’s AI workforce to fuel AI-based economic development and job creation.