The 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 wishing to undertake graduate studies at the Master's level with emphasis on artificial intelligence will be admitted by a participating department and will register in both the participating department and in the collaborative specialization.
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 advisor. 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.
Graduate Program Coordinator
Graham Taylor (3515 Thornbrough, Ext. 53644)
CARE-AI Administrative Assistant
Farrah Trahan (Ext. 56568)
This list may include Regular Graduate Faculty, Associated Graduate Faculty and/or Graduate Faculty from other universities.
Hussein A. Abdullah
B.Sc. Univ. of Technology, M.Sc., PhD Glasgow, P.Eng. - Professor
Sarah J. Adamowicz
B.Sc. Dalhousie, M.Sc. Guelph, PhD Imperial College - Associate Professor
R. Ayesha Ali
B.Sc. Western Ontario, M.Sc. Toronto, PhD Washington - Associate Professor
B.Sc. Politehnica Bucharest (Romania), M.Sc., PhD Alberta - Associate Professor
B.A.Sc. Al-Fateh, M.A.Sc. Waterloo, PhD Waterloo, P.Eng. - Professor
B.Sc. Guelph, M.Sc. Hohenheim, PhD Christian-Albrechts - Professor
B.Sc. Western, M.Sc., PhD Queen's, P.Eng - Associate Professor
David A. Calvert
BA, M.Sc. Guelph, PhD Waterloo - Associate Professor
BA, M.Sc. Bucharest, PhD Queen's - Professor
B.Sc. Shahid Teheshti, M.Sc. Guelph, PhD Waterloo - Associate Professor
B.Sc., M.Sc., PhD Guelph - Associate Professor
B.Sc. Ethiopia, M.Eng. India, PhD Concordia, P.Eng. - Associate Professor
BSE Azad, M.Sc., PhD Putra Malaysia - Associate Professor
B.Sc., M.Sc. Kuwait Univ., PhD McMaster, P.Eng. - Professor
B.A.Sc., M.A.Sc. Waterloo, PhD McMaster, P.Eng., FIET, FEC - Associate Professor
Hermann J. Eberl
Dipl. Math (M.Sc.), PhD Munich Univ. of Tech. - Professor
B.Sc. York, MMath., PhD Waterloo - Professor
B.Sc., M.Sc., PhD Saskatchewan - Associate Professor
B.Sc., M.Sc. Sharif, PhD Guelph, P.Eng. - Professor
B.Eng. Harbin Engineering, M.Sc. Tsinghua, PhD Alberta - Professor and Director
Karen D. Gordon
B.Sc. Guelph, PhD Western Ontario, P.Eng. - Professor and Associate Dean (Academic), College of Engineering and Physical Science
B.Sc. Brock, M.Sc., PhD Guelph - Associate Professor
B.Sc., M.Sc. Guelph, PhD Waterloo - Associate Professor
B.Sc. Mount Allison, BFA Nova Scotia College of Art & Design, M.Math., PhD Waterloo - Professor
Stefan C. Kremer
B.Sc. Guelph, PhD Alberta - Professor
Anna T. Lawniczak
M.Sc. Wroclaw, PhD Southern Illinois - Professor
BS, PhD Beijing - Associate Professor
B.A.Sc., PhD Queen's, P.Eng - Associate Professor
William David Lubitz
B.Sc., M.Sc., PhD California, P.Eng - Associate Professor
Lewis N. Lukens
B.Sc. Carleton College, PhD Minnesota - Professor
B.Sc., M.Sc., PhD Paul Sabatier (France) - Professor
B.A.Sc, British Columbia, S.M., C.E., PhD MIT, P.Eng. - Professor
Medhat A. Moussa
B.Sc. American, M.A.Sc. Moncton, PhD Waterloo, P.Eng. - Professor
B.Sc., M.Sc. Karachi, PhD Alberta - Assistant Professor
B.Math., Waterloo, PhD Courant Institute NYU - Assistant Professor
Charlie F. Obimbo
M.Sc. Kiev, PhD New Brunswick - Professor
Michele L. Oliver
BPE McMaster, MPE, M.Sc., PhD New Brunswick, P.Eng. - Professor
B.A.Sc., M.A.Sc. Toronto, PhD Leuven, P.Eng. - Associate Professor
B.Sc. Dalhousie, PhD Calgary - Professor
B.Sc. Jilin (China), M.Sc. Academia Sinica (China), PhD Waterloo - Associate Professor
Diplom Crete, M.A.Sc., PhD Toronto, P.Eng. - Associate Professor
Deborah A. Stacey
B.Sc. Guelph, M.A.Sc., PhD Waterloo - Associate Professor
B.Sc., M.Sc. Konstanz, PhD Goethe - Associate Director, Centre for Biodiversity, University of Guelph
Associated Graduate Faculty
B.A.Sc., M.A.Sc. Waterloo, PhD Toronto, P.Eng. - Professor
B.Sc. Burcharest, PhD British Columbia - Assistant Professor
B.Sc. Moratuwa, MES, PhD Western, P.Eng. - Associate Professor
BE Changsha, M.Sc. Peking, PhD Waterloo - Professor
B.Sc. Toronto, M.Sc., PhD Carleton - Associate Professor
B.Sc., M.Sc. BUAA (Beijing), PhD British Columbia - Retired Faculty, School of Computer Science, University of Guelph
Associated Graduate Faculty
B.Sc., M.Sc. Northwestern Polytechnical, PhD McGill - Assistant Professor
Simon X. Yang
B.Sc. Peking, M.Sc. Sinica, M.Sc. Houston, PhD Alberta, P.Eng. - Professor
MSc/MASc Collaborative Specialization
Masters students in the Collaborative Specialization in Artificial Intelligence must meet the admission requirements of the participating department in which they are enrolled. The application process has two stages. First, prospective students will apply to their primary program of interest, identifying interest in the collaborative specialization as a focus. If the student is admitted to the primary program as a thesis student, the second stage is then admission to the collaborative specialization. All applications to participate in the Collaborative Specialization in Artificial Intelligence will be vetted by the specialization’s Graduate Program Coordinator.
Upon successful completion of the collaborative specialization, graduates will have demonstrated the ability to:
- Employ common tools in artificial intelligence and machine learning (such as using data visualization techniques to perform exploratory data analysis);
- Express the mathematical foundations of artificial intelligence and machine learning, including relevant topics in calculus, linear algebra, and probability theory;
- Employ general-purpose optimizers to fit the parameters and hyper-parameters of machine learning models, and contrast the similarity and difference between machine learning and optimization;
- Master the algorithmic foundations of artificial intelligence and machine learning, identify canonical algorithmic problems, and propose existing algorithmic paradigms to solve them;
- Identify and discuss the most pertinent issues concerning artificial intelligence;
- Reflect upon and discuss ethical and social implications of artificial intelligence applications;
- Collaborate with colleagues from different backgrounds and employ multidisciplinary approach to developing design solutions to AI-related problems;
- Consider, question, and critique alternative design solutions in consideration of technical, social, and ethical themes; and
- Propose solutions to AI-related problems through written and oral forms of communication with clarity and coherency.
Masters students in the collaborative specialization in artificial intelligence must complete:
|UNIV*6080||Computational Thinking for Artificial Intelligence||0.25|
|UNIV*6090||Artificial Intelligence Applications and Society||0.50|
|One of the following Elective Core courses:|
|ENGG*6500||Introduction to Machine Learning||0.50|
|Two of the following Complementary AI-related courses: 1|
|CIS*6120||Uncertainty Reasoning in Knowledge Representation||0.50|
|CIS*6180||Analysis of Big Data||0.50|
|or DATA*6300||Analysis of Big Data|
|CIS*6190||Machine Learning for Sequential Data Processing||0.50|
|or DATA*6400||Machine Learning for Sequential Data Processing|
|CIS*6320||Image Processing Algorithms and Applications||0.50|
|ENGG*6140||Optimization Techniques for Engineering||0.50|
|ENGG*6570||Advanced Soft Computing||0.50|
|PHIL*6400||Ethics of Data Science (formerly PHIL*6760 Science and Ethics))||0.50|
|STAT*6841||Computational Statistical Inference||0.50|
|ENGG*4430||Neuro-Fuzzy and Soft Computing Systems||0.50|
|And an acceptable AI-related thesis.|
Requirements of this collaborative specialization may also serve as core and/or elective requirements in the student’s home program.
Students can elect to take a second Elective Core course in lieu of a Complementary AI-related course.
This course will provide students with an overview of the mathematical and computational foundation that is required to undertake artificial intelligence and machine learning research. Students will also gain an understanding of the historical context, breadth, and current state of the field. Students are expected to have already taken undergraduate courses in probability & statistics, calculus, linear algebra, and data structures & algorithms (STAT*2120, MATH*1210, ENGG*1500, and CIS*2520, or equivalents).
This multidisciplinary, team-taught course provides an in-depth study of how artificial intelligence methodologies can be applied to solve real-world problems in different fields. Students will work in groups to propose solutions whilst considering social and ethical implications of artificial intelligence technologies.
An examination of Artificial Intelligence principles and techniques such as: logic and rule based systems; forward and backward chaining; frames, scripts, semantic nets and the object-oriented approach; the evaluation of intelligent systems and knowledge acquisition. A sizeable project is required and applications in other areas are encouraged.
The aim of this course is to provide students with an introduction to algorithms and techniques of machine learning particularly in engineering applications. The emphasis will be on the fundamentals and not specific approach or software tool. Class discussions will cover and compare all current major approaches and their applicability to various engineering problems, while assignments and project will provide hands-on experience with some of the tools.
Topics include: nonparametric and semiparametric regression; kernel methods; regression splines; local polynomial models; generalized additive models; classification and regression trees; neural networks. This course deals with both the methodology and its application with appropriate software. Areas of application include biology, economics, engineering and medicine.
This course presents a selection of advanced approaches for the statistical analysis of data that arise in bioinformatics, especially genomic data. A central theme to this course is the modelling of complex, often high-dimensional, data structures.
Artificial neural networks, dynamical recurrent networks, dynamic input/output sequences, communications signal identification, syntactic pattern recognition.
Data mining and bioinformatics, molecular biology databases, taxonomic groupings, sequences, feature extraction, Bayesian inference, cluster analysis, information theory, machine learning, feature selection.
This course will discuss problems where optimization is required and describes the most common techniques for discrete optimization such as the use of linear programming, constraint satisfaction methods, and genetic algorithms.
This course introduces the student to basic genetic algorithms, which are based on the process of natural evolution. It is explored in terms of its mathematical foundation and applications to optimization in various domains.
Representation of uncertainty, Dempster-Schafer theory, fuzzy logic, Bayesian belief networks, decision networks, dynamic networks, probabilistic models, utility theory.
Intelligent systems consisting of multiple autonomous and interacting subsystems with emphasis on distributed reasoning and decision making. Deductive reasoning agents, practical reasoning agents, probabilistic reasoning agents, reactive and hybrid agents, negotiation and agreement, cooperation and coordination, multiagent search, distributed MDP, game theory, and modal logics.
This course concentrates on the theoretical and practical issues related to the design and study of interactive technologies for human use. Topics include: general principles of design, qualitative and quantitative research methods, prototyping techniques, theoretical issues underlying designing to individuals and groups, and ethical issues related to conducting research involving humans.
This course introduces software tools and data science techniques for analyzing big data. It covers big data principles, state-of-the-art methodologies for large data management and analysis, and their applications to real-world problems. Modern and traditional machine learning techniques and data mining methods are discussed and ethical implications of big data analysis are examined. May be offered in conjunction with DATA*6300.
This course emphasizes machine learning for sequential data processing. It covers common challenges and pre-processing techniques for sequential data such as text, biological sequences, and time series data. Students are exposed to machine learning techniques, including classical methods and more recent deep learning models, so that they obtain the background and skills needed to confront real-world applications of sequential data processing. May be offered in conjunction with DATA*6400.
Brightness transformation, image smoothing, image enhancement, thresholding, segmentation, morphology, texture analysis, shape analysis, applications in medicine and biology.
Neural networks, artificial intelligence, connectionist model, back propagation, resonance theory, sequence processing, software engineering concepts.
Computer vision studies how computers can analyze and perceive the world using input from imaging devices. Topics covered include image pre-processing, segmentation, shape analysis, object recognition, image understanding, 3D vision, motion and stereo analysis, as well as case studies.
This course serves as a graduate introduction into combinatorics and optimization. Optimization is the main pillar of Engineering and the performance of most systems can be improved through intelligent use of optimization algorithms. Topics to be covered: Complexity theory, Linear/Integer Programming techniques, Constrained/Unconstrained optimization and Nonlinear programming, Heuristic Search Techniques such as Tabu Search, Genetic Algorithms, Simulated Annealing and GRASP.
Neural dynamics and computation from a single neuron to a neural network architecture. Advanced neural networks and applications. Soft computing approaches to uncertainty representation, multi-agents and optimization.
This course covers the fundamentals of algorithms and computer programming. This may include computer arithmetic, complexity, error analysis, linear and nonlinear equations, least squares, interpolation, numerical differentiation and integration, optimization, random number generators, Monte Carlo simulation; case studies will be undertaken using modern software.
A study of the basic concepts in: linear programming, convex programming, non-convex programming, geometric programming and related numerical methods.
The process of phenomena and systems model development, techniques of model analysis, model verification, and interpretation of results are presented. The examples of continuous or discrete, deterministic or probabilistic models may include differential equations, difference equations, cellular automata, agent based models, network models, stochastic processes.
A study of the philosophical implications (ethical, legal, social, political, epistemological, etc.) of recent developments in data science, artificial intelligence, and machine learning.
Topics include the Poisson process, renewal theory, Markov chains, Martingales, random walks, Brownian motion and other Markov processes. Methods will be applied to a variety of subject matter areas. Offered in conjunction with STAT*4360. Extra work is required for graduate students.
This is an advanced course in multivariate analysis and one of the primary emphases will be on the derivation of some of the fundamental classical results of multivariate analysis. In addition, topics that are more current to the field will also be discussed such as: multivariate adaptive regression splines; projection pursuit regression; and wavelets. Offered in conjunction with STAT*4350. Extra work is required for graduate students.
This course covers Bayesian and likelihood methods, large sample theory, nuisance parameters, profile, conditional and marginal likelihoods, EM algorithms and other optimization methods, estimating functions, Monte Carlo methods for exploring posterior distributions and likelihoods, data augmentation, importance sampling and MCMC methods.
Undergraduate Complementary AI-related Courses
|ENGG*4430||Neuro-Fuzzy and Soft Computing Systems||0.50|