Collaborative Specialization in Artificial Intelligence (M.Sc./M.A.Sc.)

About the Program
The University of Guelph’s Collaborative Specialization in Artificial Intelligence (AI) provides thesis-based Master’s students with a diverse and comprehensive knowledge base in 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.

We are a Vector Institute-Recognized Masters Program
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. You can find more information at the Vector Institute website.

Affiliated Programs (Thesis-Based)
• Master of Applied Science in Engineering
• Master of Science (MSc) in Bioinformatics
• MSc in Computer Science
• MSc in Mathematics and Statistics

Students We Attract
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.

Course List

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*6100 Parallel Processing Architectures
• CIS*6120 Uncertainty Reasoning in Knowledge Representation
• CIS*6140 Software Engineering
• CIS*6160 Multi-agent Systems
• CIS*6320 Image Processing Algorithms and Applications
• CIS*6420 Soft Computing
• 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*6760 Science and Ethics
• STAT*6841 Computational Statistical Inference

Types of Careers
Graduates will be in-demand by government and private industry, for positions that include: machine learning researcher, computer vision engineer, data engineer, data scientist, software engineer, statistician.

Affiliated Faculty
If you are interested in pursuing an AI-related Masters 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 check if they are currently accepting students.

• Sarah Adamowicz, Associate Professor, Integrative Biology, Bioinformatics Graduate Program  
• Ayesha Ali, Associate Professor, Mathematics and Statistics, Bioinformatics Graduate Program 
• Luiza Antonie, Assistant Professor, School of Computer Science 
• Shawki M Areibi, Professor, School of Engineering 
• Daniel Ashlock, Professor, Mathematics and Statistics, Bioinformatics Graduate Program   
• Christine Baes, Assistant Professor, Animal Biosciences, Bioinformatics Graduate Program 
• Mohammad Biglarbegian, Associate Professor, School of Engineering 
• David A Calvert, Associate Professor, School of Computer Science  
• Monica Cojocaru, Associate Professor, Mathematics and Statistics
• Christopher Collier, Assistant Professor, School of Engineering 
• Rozita Dara, Assistant Professor, School of Computer Science
• Fantahun Defersha, Associate Professor, School of Engineering 
• Ali Dehghantanha, Assistant Professor, School of Computer Science
• Bob Dony, Associate Professor, School of Engineering 
• Hermann Eberl, Professor, Mathematics and Statistics, Bioinformatics Graduate Program  
• Zeny Feng, Associate Professor, Mathematics and Statistics, Bioinformatics Graduate Program   
• David Flatla, Associate Professor, School of Computer Science
• Andrew Gadsden, Associate Professor, School of Engineering 
• Bahram Gharabaghi, Professor, School of Engineering 
• 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 
• Hadis Karimipour, Assistant Professor, School of Engineering 
• Stefan C Kremer, Professor, School of Computer Science, Bioinformatics Graduate Program 
• Anna Lawniczak, Professor, Mathematics and Statistics
• William Lubitz, Associate 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 
• Charlie Obimbo, Associate Professor, School of Computer Science
• Michele Oliver, Professor, School of Engineering 
• Stacey Scott, Associate Professor, School of Computer Science
• Fei Song, Associate Professor, School of Computer Science
• Petros Spachos, Assistant Professor, School of Engineering 
• Deborah A Stacey, Associate Professor, School of Computer Science
• Graham Taylor, Associate Professor, School of Engineering, Bioinformatics Graduate Program; Program Director, Collaborative Specialization in Artificial Intelligence 
• 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 
• Simon Yang, Professor, School of Engineering   
• Yang Xiang, Professor, School of Computer Science  
• Dirk Steinke, Adjunct Professor, Integrative Biology, Bioinformatics Graduate Program