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2023-2024 nominations open soon
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. More details on eligibility criteria can be found here.
Why study in Ontario?
Over 2000 students have graduated from Vector’s recognized Master’s programs in Ontario. The province is also home to 40% of the country’s population and has more international students than any other province, it borders the wilderness, Great Lakes, and major US cities.
Here’s why you should pursue your Master’s in Ontario:
– In 2020/2021 an estimated 7,253 AI jobs were created in Ontario
– In 2020/2021 an estimate $2.16 billion was invested into Ontario AI ecosystem from VCs
– In 2020/2021 there was an estimate 5.5X-6.3X increase in R&D spending on Ontario AI
– Top AI researchers like Geoffrey Hinton base their research in Ontario
– Canada ranks 4th in the world for AI investment, innovation and implementation
There are additional benefits to becoming a Vector scholarship recipient
– Exclusive networking & recruitment events
– Access to Vector’s Talent Hub, a curated job board for the Vector community, with employers and roles across Ontario in AI and data science
– Professional development opportunities
– Access to research talks
Vector’s recognized master’s programs
There are currently over 25 master’s programs in Ontario recognized by the Vector Institute for equipping graduates with AI skills and competencies sought by industry. Both prospective master’s students applying to Vector recognized programs or pursuing individual AI study paths may be eligible for the scholarship.
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.
The University of Guelph’s Master of Data Science (MDS) is a one-year, course-based program that runs September to August. The MDS program trains individuals to become computationally-skilled and ethically-minded data analysts with strong communications skills. It focuses on data mining; data warehousing and database management; extraction, transformation, and loading (ETL); machine learning and artificial intelligence; statistical modelling; scripting; and data visualization. Notably, the MDS program features a unique emphasis on spatial-temporal data. With the advent of Internet of Things, more businesses are collecting data over time and across geographic maps. Thus, a data science program that prepares students for the nuances of spatial-temporal data will be an asset as graduates begin careers in industry. Moreover, students will be introduced to concepts around data privacy and security, as well as ethical issues related to each step of the data life cycle. Students will be expected to design, implement, visualize and present their data analyses—which address complex problems relevant to industry partners—to a diverse audience. Finally, the hands-on delivery format and built-in experiential learning opportunities will foster the development of soft skills, such as communication and problem solving, that are critical for graduates to successfully transition into the workforce.
Master of Science (Computer Science – AI)
The Department of Computer Science is offering a two-year, thesis-based Master of Science in Computer Science (MSc) program with a specialization in Artificial Intelligence (AI).
Students will develop the skills and knowledge to conduct research in the field of Computer Science with a focus on core AI techniques. Upon completion, students will be able to apply and select appropriate AI algorithms and techniques in a variety of industrial sectors and further advance the AI related research. Topics will include natural language processing, deep learning, machine learning, image processing, pattern recognition and other emerging technologies. Finally, students will develop research and application based ethical awareness.
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.
The Master of Business Analytics and AI (MBAI) prepares students for highly successful careers both in the private and public sector where data is used to make decisions. The MBAI is a market-driven degree in an in-demand field at the intersection of technology and business. As a student in this program, you will be equipped with the skills, knowledge and networks needed to succeed in today’s workplace as well as the ability to adapt to the ever-evolving workplace of the future. You will be able to develop your
strengths in digital economy, data analytics and artificial intelligence, all while building community and expanding connections with reputable industry partners.
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.
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.
Technology has undeniably changed how financial markets and institutions function. Every part of the financial value chain is being disrupted by nimble technology-based innovators and changing the skills required to be successful in modern finance.
The Master of Financial Innovation & Technology provides a solution to the fast-growing need in the financial sector for leaders with a solid understanding of finance, data science and machine learning. The program will equip students with a unique combination of skills consisting of a deep understanding of financial models, data and technology. Take this 14-month program while working, with evening and weekend classes based at Smith Toronto, a dedicated learning facility in downtown Toronto.
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.
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.
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.
Program description coming soon.
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.
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.
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.
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.
This collaborative specialization is an interdisciplinary training program that equip students with the expertise to apply modern machine learning approaches to improve human health and well-being. Innovations in health and biomedical science are increasingly driven by large sets of genetic, physiological, imaging, and behavioral data, and the algorithms necessary to analyze them. The program is open to students in a number of graduate programs across the Faculties of Science, Health Sciences, Engineering, and the Schulich School of Dentistry and Medicine. Students in the collaborative specialization complete a thesis in their program, and take one foundational course and one applied course in machine learning. Special training is provided in project development, end-user engagement, and a reflected practice that considers the ethical implication of the developed techniques.
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.
York University – Schulich School of Business
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.