University of Guelph
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 Science (Computer Science – AI)
Description: Program description to be made available soon
Ontario Tech University
Description: Program descriptions to be made available soon
University of Ottawa Université d’Ottawa
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 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.
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: 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 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)
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.
University of Toronto (Institute of Health Policy, Management, Evaluation at Dalla Lana School of Public Health)
Program: Master of Health Informatics
Description: 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.
University of Waterloo
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.
University of Waterloo
Description: 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.
University of Waterloo
Description: 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, that is, 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.
Description: 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 consists 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.
Description: 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 which 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.
Program: Master of Science (Computer Science – AI)
Description: Program descriptions to be made available soon
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
Program: Master of Business Analytics
Description: 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.
Description: 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 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.
* Note: Launch pending approval from the Ontario Universities’ Council on Quality Assurance