Collaborative Specialization in Artificial Intelligence (M.Sc./M.A.Sc.)
Become an expert in one of the world's foremost and fastest-growing areas of technology.
The University of Guelph’s Collaborative Specialization in Artificial Intelligence (CSAI) provides thesis-based master’s students with a diverse and comprehensive knowledge base in artificial intelligence (AI). Students 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 obtain broad expertise in machine learning and AI, including essential skills in programming and algorithmic thinking, mathematical foundations and statistical analysis for AI, optimization, and data visualization. Students also develop an intimate understanding about the policy, regulatory and ethical issues related to AI and its uses.
"I really enjoyed my undergraduate experience at Guelph, which included multiple research co-op work terms. I was also especially interested in the Collaborative Specialization in Artificial Intelligence and my ability to integrate AI with my thesis in the field of Environmental Engineering.
My background in Environmental Engineering has lead me to pursue ways of improving our understanding of the complexities involved in environmental systems. Specifically, the goal of my research is to improve our understanding of flooding events, the specific mechanisms leading to these events, and the consequential impacts on human life and the surrounding environment."
Environmental Engineering (MASc), Collaborative Specialization in Artificial Intelligence
The CSAI program is ideal for students interested in gaining advanced training and research opportunities in artificial intelligence, machine learning, bioinformatics, neural networks and deep learning, optimization, software engineering, image processing, among other AI-related focus areas.
Graduates are in-demand by government and private industry, in positions that include machine learning researchers, computer vision engineers, data engineers and data scientists, software engineers, statisticians, and more.
Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) Affiliation
CARE-AI's mission is to advance multidisciplinary AI training and research and its responsible application to improve life. With their shared focus on AI expertise, research and ethics, CSAI is an affiliate program of CARE-AI. Rather than considering ethics or responsibility as an afterthought at deployment time or thesis time, U of G embeds key issues such as fairness and bias, accountability, transparency and ethics from the start of the student's program in partnership with CARE-AI, beginning with UNIV*6080 Computational Thinking in AI.
In addition to program requirements, CARE-AI hosts event series focused on the "bigger picture" in developing and deploying AI systems. This is a unique forum for grad students, faculty, and community members across all disciplines to engage on these issues.
Vector Institute Affiliation
The CSAI program is a Vector Institute affiliated program. Its students become part of the Vector Institute’s community of renowned researchers, major Canadian companies, and AI startups solving high-impact problems.
Vector Scholarship in Artificial Intelligence
Students who are enrolled in the University of Guelph Collaborative Specialization in Artificial Intelligence are eligible to apply for Vector Scholarships in Artificial Intelligence, valued at $17,500 each. Both domestic and international students with first class standing (minimum A- in their last two years of full-time study) who have applied to the Specialization are eligible for consideration. For consideration in the 2022 VSAI cycle, you must submit all nomination materials to email@example.com by Sunday, February 13, 2022.
Networking and Events
The Vector Institute’s exclusive events put you face-to-face with AI teams from major Canadian employers, providing unique access to career opportunities. Build relationships with a network of AI professionals that can become a spring of new opportunity, insight, and collaboration over your career.
Digital Talent Hub
Discover career opportunities in the Vector Institute’s extensive industry network through the Digital Talent Hub, an exclusive online platform that curates AI-related job openings among top Canadian employers. Available only to the Vector community, the Digital Talent Hub is trusted by hiring managers and connects talent with a wealth of high-impact internship and full-time openings at leading companies.
Students We Attract
Our students are top domestic and international students from undergraduate and graduate programs that include: Biostatistics; Computer Sciences; Computer, Electrical, Electronic, Environmental, Information Technology, Software, and Systems Engineering; Mathematics; and Physics.
What Our Students Are Saying
Statistics (MSc), Collaborative Specialization in Artificial Intelligence
With an increase in data, improving technology and computational power, artificial intelligence will continue to evolve and be an aid in many issues. Although the idea of artificial intelligence taking over can seem daunting to individuals, I believe that if different fields collaborate to make ethical algorithms, it will benefit society in ways we can’t even imagine right now. Particularly in healthcare, AI has the potential to be a powerful tool in diagnosis, caretaking, and risk predictions.
Statistics and artificial intelligence excite me because it is interesting how you can use them to understand the relationship between variables or even make predictions for other observations. I always found enjoyment in analyzing data to answer research questions. The analysis in research is the most exciting part since it is when you find out whether your hypothesis was correct or if there is something else that can be observed in the data. The more I learn about statistics and artificial intelligence, the more excited I get by the potential of it and how it can be used. Read Alysha's full story.
About the Specialization
How does the program specialization work?
Prospective students must first meet the admission requirements of a participating home program (see: affiliated programs). Once the student is admitted to a home program, their application will be forwarded to the Collaborative Specialization’s Graduate Program Coordinator for review.
Students enrolled in the specialization must complete at least 2.25 credits in AI-related courses, as well as their home program requirements. Note that some credits in AI-related courses can satisfy both Collaborative Specialization in AI and home program requirements.
Masters students in the collaborative specialization in artificial intelligence must complete the following:
- UNIV*6080 Computational Thinking for Artificial Intelligence
- UNIV*6090 Artificial Intelligence and Society
One of the following elective core courses:
- CIS*6020 Artificial Intelligence
- ENGG*6500 Introduction to Machine Learning
- STAT*6801 Statistical Learning
Two of the following complementary AI-related courses**:
- BINF*6970 Statistical Bioinformatics
- CIS*6050 Neural Networks
- CIS*6060 Bioinformatics
- CIS*6070 Discrete Optimization
- CIS*6080 Genetic Algorithms
- CIS*6120 Uncertainty Reasoning in Knowledge Representation
- CIS*6160 Multiagent Systems
- CIS*6170 Human-Computer Interaction
- CIS*6180/DATA*6300 Analysis of Big Data
- CIS*6190/DATA*6400 Machine Learning for Sequential Data Processing
- CIS*6320 Image Processing Algorithms and Applications
- CIS*6420 Soft Computing
- ENGG*4460 Robotic Systems
- ENGG*6090 Image Analysis
- ENGG*6100 Machine Vision
- ENGG*6140 Optimization Techniques for Engineering
- ENGG*6570 Advanced Soft Computing
- MATH*6020 Scientific Computing
- MATH*6021 Optimization I
- MATH*6051 Mathematical Modelling
- PHIL*6400 Ethics of Data Science (formerly PHIL*6760 Science and Ethics)
- STAT*4000 Statistical Computing
- STAT*6721 Stochastic Modelling
- STAT*6821 Multivariate Analysis
- STAT*6841 Computational Statistical Inference
**Note: CSAI students can elect to take a second "elective core course" in lieu of a complementary AI-related course.
If you are interested in pursuing an AI-related master's thesis at the University of Guelph, then please contact one of the below-listed faculty members to see if your research interests align and to confirm if they are accepting students:
- Sarah Adamowicz, Associate Professor, Integrative Biology, Bioinformatics Graduate Program
- Ayesha Ali, Associate Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Luiza Antonie, Associate Professor, School of Computer Science
- Shawki M Areibi, Professor, School of Engineering
- Christine Baes, Associate Professor, Animal Biosciences, Bioinformatics Graduate Program
- Mohammad Biglarbegian, Associate Professor, School of Engineering
- Scott Brandon, Assistant Professor, School of Engineering
- Neil Bruce, Associate Professor, School of Computer Science
- David A Calvert, Associate Professor, School of Computer Science
- Monica Cojocaru, Professor, Mathematics and Statistics
- Rozita Dara, Associate Professor, School of Computer Science
- Lorna Deeth, Assistant Professor, Mathematics and Statistics
- Fantahun Defersha, Associate Professor, School of Engineering
- Ali Dehghantanha, Associate Professor, School of Computer Science
- Ibrahim Deiab, Professor, School of Engineering
- Bob Dony, Associate Professor, School of Engineering
- Hermann Eberl, Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Zeny Feng, Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- David Flatla, Associate Professor, School of Computer Science
- Bahram Gharabaghi, Professor, School of Engineering
- Minglun Gong, Professor, School of Computer Science
- Karen Gordon, Associate Professor, School of Engineering
- Gary Grewal, Associate Professor, School of Computer Science
- Andrew Hamilton-Wright, Associate Professor, School of Computer Science, Bioinformatics Graduate Program
- Julie Horrocks, Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Stefan C Kremer, Professor, School of Computer Science, Bioinformatics Graduate Program
- Anna Lawniczak, Professor, Mathematics and Statistics
- Lei Lei, Associate Professor, School of Engineering
- William Lubitz, Professor, School of Engineering
- Lewis Lukens, Associate Professor, Plant Agriculture, Bioinformatics Graduate Program
- Pascal Matsakis, Professor, School of Computer Science
- Ed McBean, Professor, School of Engineering
- Medhat Moussa, Professor, School of Engineering
- Khurram Nadeem, Assistant Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Mihai Nica, Assistant Professor, Mathematics and Statistics
- Charlie Obimbo, Professor, School of Computer Science
- Michele Oliver, Professor, School of Engineering
- Rafael M. Santos, Assistant Professor, School of Engineering
- Stacey Scott, Professor, School of Computer Science
- Fei Song, Associate Professor, School of Computer Science
- Petros Spachos, Associate Professor, School of Engineering
- Deborah A Stacey, Associate Professor, School of Computer Science
- Graham Taylor, Professor, School of Engineering, Bioinformatics Graduate Program
- Dan Tulpan, Assistant Professor, Animal Biosciences, Bioinformatics Graduate Program
- Eran Ukwatta, Assistant Professor, School of Engineering
- Fangju Wang, Professor, School of Computer Science
- Mark Wineberg, Associate Professor, School of Computer Science
- Sheng Yang, Assistant Professor, School of Engineering
- Simon Yang, Professor, School of Engineering
- Yang Xiang, Professor, School of Computer Science
- Dirk Steinke, Adjunct Professor, Integrative Biology, Bioinformatics Graduate Program
Program Director and Graduate Program Coordinator:
Graduate Program Assistant:
Please direct all inquiries to: