Linear systems and signal sampling, Fourier transforms and frequency analysis, Laplace transforms, FFT and inverse FFT algorithms, convolution/deconvolution, impulse response, random signals, noise characterization, auto- and cross-correlation, power spectra, adaptive filters, detection and clustering. These topics will be covered with extensive coverage on their applications to various topics in mechanical or biomedical engineering. In mechanical engineering such topics include vibrations, signal timing, spectral/phase analysis, signature analysis, thermal waves, acoustic emission, engine performance analysis, resonant acoustic spectroscopy (RAS), crack detection and location with ultrasound, flow measurements, condition-based monitoring and maintenance, fracture mechanics, etc. In biomedical engineering these topics include modeling of biomedical control systems, analysis of evoked potentials, analysis of electroencephalograms and electrocardiograms.

Field research is a process where data is collected through qualitative methods. The objective of field study is to observe and interpret a subject of study in its natural environment. Field research employs qualitative methods, including interviews, direct observation, focus groups, and artifact analyses. In human factors, field work is used in a variety of ways to identify errors in systems, understand human needs, and to evaluate designs. In this course, students will learn core qualitative methodologies, philosophies, and their application to human factors contexts and problems. Learners will critique qualitative methodologies and have an opportunity to practice techniques through the design of a field research study.

Information Engineering focuses on the representation and use of information in the context of the web. The first part of the course covers the Semantic Web, including XML, RDF, Linked Data, Provenance, Trust and Data Mashup. The second part covers web-based Knowledge Representations, including: Description Logic, OWL, SWRL, and Ontologies.

To remain competitive, enterprises must become increasingly agile and integrated across their functions. Enterprise models play a critical role in this integration, enabling improved designs for enterprises, analysis of their performance, and management of their operations. This course motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modelling, including emerging standards and implementation technologies.

This course will explore theoretical techniques for the design and analysis of formal ontologies. Topics will include the design of verified ontologies, methodologies for proving properties about ontologies, and applications of classification theorems from mathematics. These techniques will be applied to ontologies that are currently being used in government and industry.

This course is a research seminar that focuses on recent developments in the area of Data Management for Analytics. Science, businesses, society, and government are been revolutionized by data-driven methods that benefit heavily from scalable data management techniques. The course provides an overview of data management concepts applied to analytics, covering methods and techniques, including distributed computations on massive datasets and frameworks for enabling large-scale parallel data processing on clusters of commodity servers. Emphasis is given to data management techniques for analyzing Web Data and Open Datasets. The course evaluation is based on student presentations, a focused bibliography survey, a hands on invigilated lab, and a course project (the last two using computational notebooks on scalable platforms). The project goal is to reproduce high quality published research in the area of data analytics, emphasizing data management aspects.

This course provides students with an understanding of the role of a decision support system in an organization, its components, and the theories and techniques used to construct them. The course will focus on information analysis to support organizational decision-making needs and will cover topics including information retrieval, descriptive and predictive modeling using machine learning and data mining, recommendation systems, and effective visualization and communication of analytical results.

The course objective is to familiarize students with the principles and methods of systems engineering in the design of products. It includes specific practical examples and projects to aid in understanding and appreciating fundamental principles. Students will apply the various systems engineering methods and techniques as appropriate across all phases of a product’s life cycle. The course will prepare students who are or will be involved in high technology complex systems, and the preliminary and detailed design of products.

This Research Course will provide students with the conceptual, theoretical, and implementational foundations of fundamental tools for structured learning and inference: probabilistic graphical models, probabilistic programming, and deep neural networks. The course will focus on the design and training of structured models for specific application use cases such as answering probabilistic queries over data, sequence tagging and classification, and image recognition through programming intensive projects including a final independently proposed research project with report component.

This course will provide an overview of deep learning techniques with engineering applications. Topics covered include: neural network architectures; model training and regularization; data augmentation; transfer learning; generative models; and a brief overview of reinforcement learning. Ethics and fairness will play a prominent role in the course discussions. The course will follow an applied approach through several skill building assignments and a team-based project.

Complex data in a variety of applications can often lend themselves to a sequence or graph representation. In recent years, many tools and techniques were developed to efficiently learn from sequence and graph data. In particular, specialized deep neural network architectures, such as graph neural networks and transformers, have obtained state-of-the-art performance in tasks such as natural language processing and recommender systems. This course will provide students with advanced conceptual, theoretical, and implementational skills for developing machine learning approaches for processing sequences and graphs. The course will cover the design and training of both fundamental models and recent state-of-the-art models, and will prepare students to conduct research that involves the development or application of machine learning techniques for sequences or graph data. Knowledge of machine learning, algorithms, and programming is required, while knowledge of deep learning is recommended.

Formulation of integer programming problems and the characterization of optimization problems representable as integer and mixed-integer programs. The degree of difficulty of classes of integer programs and its relation to the structure of their feasible sets. Optimality conditions. Branchand-bound, cutting plane, and decomposition methods for obtaining solutions or approximating solutions.

This course is an introduction to stochastic processes with an emphasis on applications to queueing theory and service engineering.

A course in renewal theory, Markov renewal theory, regenerative and semi-regenerative processes, Markov and semi-Markov processes and decision processes with emphasis on applications in production/inventory control, maintenance, communication systems, flexible manufacturing systems.

Official course description: Stochastic programming and robust optimization are optimization tools dealing with a class of models and algorithms in which data is affected by uncertainty, i.e., some of the input data are not perfectly known at the time the decisions are made. Topics include modeling uncertainty in optimization problems, two-stage and multistage stochastic programs with recourse, chance constrained programs, computational solution methods, approximation and sampling methods, and applications. Knowledge of linear programming, probability and statistics are required, while programming ability and knowledge of integer programming are helpful.

This course is an introduction to modelling and analysis of stochastic dynamical systems using computer simulation. The course will provide a rigorous yet accessible treatment of the probability foundations of simulation, and discuss programming simulation models in a lower-level language (e.g., Python). Design and analysis of simulation experiments will also be covered. Applications in service and financial engineering will be emphasized.

This is a course to introduce the students to theories of Markov decision processes. Emphasis will be on the rigorous mathematical treatment of the theory of Markov decision processes. Topics will include MDP finite horizon, MDP with infinite horizon, and some of the recent development of solution method.

This is a seminar-based course in which we will review a variety of papers in the field of healthcare OR. We will survey and evaluate several papers within topic areas and try to identify areas for potential future research. Some papers will be distinctly OR, while others will come from researchers in the field of health policy and health economics. One thing that you will notice as we go through the literature is that the area of healthcare engineering is interdisciplinary in nature and encourages solutions that are derived from various areas of expertise. This interdisciplinary approach is also encouraged through the many funding bodies that currently support healthcare engineering research in North America. The Canadian Institute of Health Research (CIHR) funds the majority of healthcare research in Canada. It is composed of 14 virtual 'institutes' that represent all facets of health research. The Institute of Health Services and Policy Research, IHSPR, is most related to the type of collaborative research discussed above. It supports innovative research, capacity-building, and knowledge translation in order to improve health care service delivery. In 2001 and 2004, IHSPR was involved with national consultations on health services priorities entitled "Listening for Direction." The result of these consultations was a set of priorities for Canadian researchers in the area of health care policy and management. Of course, not all of the topics are relevant to Healthcare Engineering, but many of the readings and articles discussed in this class will align with the most recent set of priorities.

The topic of this course is the "non-traditional" optimization technique Constraint Programming (CP) and hybrids of CP with approaches in OR. Heavy emphasis will be placed on similarities and differences between CP and mathematical programming including the unified framework of search, relaxation, and inference. The primary hybrid approaches will be based on constraint generation approaches including Logic-based Benders Decomposition and SAT Modulo Theory. This is an advanced graduate level course intended for research-stream students. MEng students are not admitted without special permission from the instructor. The course will be challenging. Students are expected to read material in preparation for each lecture and, in a few cases, view online lectures. An objective of this course is to impart skills necessary for an academic career such as paper writing, presentation skills, and writing peer reviews. The main evaluation will be a project where the student is expected to apply techniques discussed in the course to their own research interests: you should do something you weren’t already planning to do as part of your research. A goal of this course is that these projects will be publishable in a peer-reviewed forum.

Rigorous introduction to the theory of linear programming. Simplex method, revised simplex method, duality, dual simplex method. Post-optimality analysis. Interior point methods. Decomposition methods. Network flow algorithms. Maximum flow, shortest path, assignment, min cost flow problems.

Theory and computational methods of non-linear optimization. Convex sets, convex and concave functions. Unconstrained and Constrained Optimization. Quadratic Programming. Optimality conditions and convergence results. Karush-Kuhn-Tucker conditions. Introduction to penalty and barrier methods. Duality in nonlinear programming.

The objective of the course is to examine the construction of computational algorithms in solving financial problems, such as risk-aware decision-making, asset pricing, portfolio optimization, and hedging. Considerable attention is devoted to the application of computational and programming techniques to financial, investment and risk management problems. Materials in this course are quantitative and computational in nature as well as analytical. Topics include mean-variance portfolio optimization, simulation (Monte Carlo) methods, scenario-based risk optimization, hedging, uncertainty modeling, asset pricing, simulating stochastic processes, and numerical solutions of differential equations. Python is the primary computational and modeling software used in this course, we also briefly describe other programming environments such as R, Matlab and C/C++ used in financial engineering. Practical aspects of financial and risk modeling, which are used by industry practitioners, are emphasized.

This course illustrates the use of industrial engineering techniques in the field of healthcare. Common strategic, tactical, and operational decision-making problems arising in healthcare will be approached from an operations research perspective. Unique aspects of healthcare compared to other industries will be discussed. Real-world datasets will be provided to illustrate the complexity of applying standard operations research methods to healthcare. A background in operations research is required.

The objective of the course is to learn analytical models and overview quantitative algorithms for solving engineering and business problems. Data science or analytics is the process of deriving insights from data in order to make optimal decisions. It allows hundreds of companies and governments to save lives, increase profits and minimize resource usage. Considerable attention in the course is devoted to applications of computational and modeling algorithms to finance, risk management, marketing, health care, smart city projects, crime prevention, predictive maintenance, web and social media analytics, personal analytics, etc. We will show how various data science and analytics techniques such as basic statistics, regressions, uncertainty modeling, simulation and optimization modeling, data mining and machine learning, text analytics, artificial intelligence, and visualizations can be implemented and applied using Python. Python and IBM Watson Analytics are modeling and visualization software used in this course. Practical aspects of computational models and case studies in Interactive Python are emphasized.

This is a 12-week course designed to provide an understanding of fundamentals of Machine Learning (ML) for wide applications in Medical Imaging. Medical Imaging, which is an important specialty in medicine for diagnosis, prognosis, and intervention of different types of diseases including cancer, is increasingly moving toward quantitative approaches. ML algorithms are playing a key role in quantitative medical imaging analytics for disease diagnosis (detection) and prognosis (prediction). With the help of recent advances in ML and computer vision, novel predictive models are capable of diagnosing a disease with high accuracy and consistency, and predicting clinical outcomes (e.g., response to treatment) with an accuracy, which is beyond existing clinical methods.

The goal in this course is to help students develop the fundamental skills and expertise in Quantitative Medical Imaging and Machine Learning including Deep Learning with specific applications in Diagnostic and Prognostic Solutions for Medical Imaging. It is expected the students already be familiar with the basics of machine learning and the objective of this course is to cover the fundamentals of Medical Image Analysis including Computer-aided detection and diagnosis, and the applications of ML in Medical Image Analysis.

This course will equip the students with the fundamental skills and knowledge for: understanding the statistical foundation of data science and machine learning methods; approaching active and passive data as artifacts for scientific evaluation; combining, pre-processing, and cleaning data in practical data science projects; performing exploratory data analysis and uncovering patterns in data; analyzing data and making inference using methods from statistical learning; resampling data and evaluate the error of any computational estimate; using confidence intervals, analysis of variance, and hypothesis testing to explain data; implementing linear and nonlinear regression models for prediction and inference; designing and understanding tree-based models and support vector machines; detecting and avoiding misleading statistical figures, information visualization, and other forms of data presentation which lack a logical coherence. This is an intensive and high-demand course which requires active engagement and participation.

This course covers Big Data fundamentals including an overview of Hadoop MapReduce and Spark. Covers Cloud fundamentals and Big Data Analytics on Cloud-based platforms including an introduction to a specific Cloud platform such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform along with common practices for this platform. Covers Cloud technologies to store and process structured, unstructured and semi-structured data. Covers Cloud-based implementation of Real-time Analytics and Machine Learning.

Formulation of integer programming problems and the characterization of optimization problems representable as integer and mixed-integer programs. The degree of difficulty of classes of integer programs and its relation to the structure of their feasible sets. Optimality conditions. Branchand-bound, cutting plane, and decomposition methods for obtaining solutions or approximating solutions.

Mathematical optimization algorithms are used to solve a wide variety of decision-making tasks. The design of optimization algorithms often requires substantial theoretical insights or algorithmic engineering, both of which are manual, tedious tasks. This course introduces automated machine learning approaches for improving optimization algorithms in the presence of a historical dataset or a generator of problem instances from a domain of interest. Topics include automated algorithm configuration, modeling iterative heuristics in the reinforcement learning framework, deep neural networks for modeling combinatorial optimization problems, guiding exact solvers with learned search strategies, learning-theoretic guarantees, and benchmarking/computational considerations. The focus will be on discrete optimization in the integer programming framework, both exact and heuristic. Knowledge of integer programming, algorithm design, machine learning, and programming are required, while knowledge of deep learning is helpful.