The Collaborative Specialization in Artificial Intelligence (AI) provides thesis-based masters 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 masters 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
Dr. Graham Taylor (3515 Thornbrough, Ext. 53644)
Graduate Program Assistant (, Ext. )
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
R. Ayesha Ali
B.Sc. Western Ontario, M.Sc. Toronto, PhD Washington - Associate Professor
Daniel A. Ashlock
B.Sc. Kansas, PhD CalTech - Professor and Chair
B.Sc. Guelph, M.Sc. Hohenheim, PhD Christian-Albrechts - Associate Professor
B.Sc. Tehran, MA Toronto, PhD Waterloo, P.Eng. - Associate Professor
B.Sc. Western, M.Sc., PhD Queen's, P.Eng - Assistant Professor
David A. Calvert
BA, M.Sc. Guelph, PhD Waterloo - Associate Professor
BA, M.Sc. Bucharest, PhD Queen's - Professor
B.Sc., M.Sc., PhD Guelph - Assistant Professor
BSE Azad, M.Sc., PhD Putra Malaysia - Assistant Professor
B.A.Sc., M.A.Sc. Waterloo, PhD McMaster, P.Eng., FIET, FEC - Associate Professor
B.Sc. York, MMath., PhD Waterloo - Professor
B.Sc., M.Sc., PhD Saskatchewan - Associate Professor
B.Eng. Harbin Engineering, M.Sc. Tsinghua, PhD Alberta - Professor and Director
Karen D. Gordon
B.Sc. Guelph, PhD Western Ontario, P.Eng - Associate Professor and Associate Dean (Academic), College of Engineering and Physical Science
B.Sc. Brock, M.Sc., PhD Guelph - Associate Professor
B.Sc. Mount Allison, BFA Nova Scotia College of Art & Design, MMath, PhD Waterloo - Professor
Anna T. Lawniczak
M.Sc. Wroclaw, PhD Southern Illinois - Professor
BS, PhD Beijing - Assistant Professor
William David Lubitz
B.Sc., M.Sc., PhD California, P.Eng - Associate 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, PEng - Professor
B.Sc., M.Sc. Karachi, PhD Alberta - Assistant Professor
B.Math., Waterloo, PhD Courant Institute NYU - Assistant Professor
Michele L. Oliver
BPE McMaster, MPE, M.Sc., PhD New Brunswick, P.Eng - Professor
B.Sc. Dalhousie, PhD Calgary - 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
BE Changsha, M.Sc. Peking, PhD Waterloo - 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.
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|
|Select one of the following Elective Core courses:|
|ENGG*6500||Introduction to Machine Learning||0.50|
|Select two of the following Complementary AI-related courses:|
|CIS*6120||Uncertainty Reasoning in Knowledge Representation||0.50|
|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*6760||Science and Ethics||0.50|
|STAT*6841||Computational Statistical Inference||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.
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.
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 consideration of the problems which arise in the conjunction of science and ethics.
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