Exact and numerical analysis of steady and transient conduction in solids. Solutions of one-dimensional and multidimensional systems. Principles of convection and solutions under laminar and turbulent flow over flat plates and inside and over pipes. Free convection. Thermal radiation between multiple black and grey surfaces. Analysis of open-ended design problems for improving thermal transport in commercial products.

Life Cycle Assessment for the measurement of environmental impacts of existing products and processes. Design for Environment principles for the reduction of environmental impacts in new product and process designs. Functional, economic, and societal analysis taught for use in a major team-written project to compare and contrast two product or process alternatives for a client.

Instruction and assessment of communication centered around course deliverables that will form part of an ongoing design portfolio.

Three-dimensional stress transformation, strain energy, energy methods, finite element method, asymmetric and curved beams, superposition of beam solutions, beams on elastic foundations, buckling, fracture mechanics, yield criteria, stress concentration, plane stress and strain.

This introductory course to numerical methods includes the following topics: polynomial interpolation, numerical integration, solution of linear systems of equations, least squares fitting, solution of nonlinear equations, numerical differentiation, solution of ordinary differential equations, and solution of partial differential equations. Tutorial assignments using MATLAB will focus on engineering applications relevant to the background of students taking the course.

This course presents analysis of complex circuits and application of circuit principles to design circuits for mechanical engineering systems. Discussions will center around circuits and instrumentation. In-depth discussions will be given on a number of topics: (1) Mechatronics design applications of circuit principles; (2) Network theorems, node-voltage, mesh-current method, Thévenin equivalents; (3) Operational amplifier circuits; (4) 1st and 2nd order circuits; (5) Laplace transform, frequency response; (6) Passive and active filter design (low- and high-pass filters, bandpass and bandreject filters); (7) Interface/readout circuits for mechanical engineering systems, sensors, instrumentation; (8) Inductance, transformers, DC/AC machines; (9) Digital circuit and data sampling introduction.

The Biology of Work: anatomical and physiological factors underlying the design of equipment and workplaces. Biomechanical factors governing physical workload and motor performance. Circadian rhythms and shift work. Measurement and specification of heat, light, and sound with respect to design of the work environment. The influence of practical and psychosocial factors on workplace ergonomic decision-making.

Application of information and interaction design principles in interactive systems. Focus on design and methods for understanding user needs, making sense of user research, prototyping, evaluation methods and iterative design. The course will include in depth coverage of rapid prototyping, scenario-based design, usability inspection methods, summative and formative usability evaluation, and comparison tests. Eye tracking, remote testing and experience/ journey mapping will be introduced.

A detailed analysis will be made of several cases in which human factors methods have been applied to improve the efficiency with which human-machine systems operate. Examples will be chosen both from the area of basic ergonomics and from high technology. Emphasis will be placed on the practical use of material learned in earlier human factors courses.

A study of the fundamental behaviour of the major semiconductor devices (diodes, bipolar junction transistors and field effect transistors). Development of analysis and design methods for basic analog and digital electronic circuits and devices using analytical, computer and laboratory tools. Application of electronic circuits to instrumentation and mechatronic systems.

The course covers the software lifecycle of user-centered, computer-based information systems. Topics include software development methodologies, requirement engineering, use case analysis, process modelling, data flow diagrams, UML, design, model-driven architecture, and implementation. The course will emphasize user-centered perspectives and effective communication across the software lifecycle of information systems.

This course provides an understanding of the principles and techniques of information modelling and data management, covering both relational theory and SQL database systems (DBMS), as well as entity-relation conceptual modelling. The course also provides an introduction to graph databases (RDF, SPARQL, and knowledge graphs), as well as UML class diagrams. The laboratory focuses on database application development using SQL DBMS, OLAP queries and data modelling.

This course focuses on understanding and applying multiple perspectives for organizing, assessing, designing, and implementing integrated distributed information systems to support an organization's objectives. The emphasis is on 1) understanding how Business Process Management techniques and tools can contribute to align an organization's business and information technology perspectives; 2) designing, developing, and deploying Business Processes as information systems. The course introduces blockchain technologies, an emerging class of distributed information system providing the foundation for Web3 decentralized applications. Students will work in the laboratory to develop business processes that integrate blockchain smart contracts, specified using the BPMN industry standard notation. Students will implement and test executable BPMN business processes using an open source BPMN engine together with additional Java programming.

This course provides students with knowledge and skills for understanding, analyzing, and solving decision making problems which involve economic concepts. These problems deal with deciding among alternatives in engineering projects with respect to costs and benefits over time. The overarching goal of the course is preparing engineers with the skills and knowledge for analyzing economic decisions quantitatively and making suitable decisions by acknowledging and incorporating the ramifications of factors like interest, depreciation, taxes, inflation, and risk in engineering projects.

Study of work systems design in new and existing organizations. Consideration will be given to fundamental organizational theory topics such as structure, lifecycle, culture, and ethics. These concepts will be the foundation for an understanding of concepts such as bureaucracy, incentives, innovation, international business, trends in technology, and hiring. An emphasis will be placed on applying these concepts to real-world organizational examples and case studies.

Principles for developing, testing and using discrete event simulation models for system performance improvement. Simulation languages, generating random variables, verifying and validating simulation models. Statistical methods for analyzing simulation model outputs, and comparing alternative system designs. Fitting input distributions, including goodness of fit tests. Role of optimization in simulation studies.

This course focuses on features of production/service systems and methods of modelling their operation; the material flow, information flow and control systems. Topics include demand forecasting, inventory management, supply chain management, capacity planning, and lot size planning. Emphasis will be placed on the modelling aspects of operations management, as well as the application of analytical methods in the design of production/service systems. Students will be asked to address open-ended design problems in various activities of the course.

Linear programming extensions: goal programming, column generation, interior point solution methods, game theory applications, quadratic programming, bi-level programming, stochastic programming. Mathematical Programming formulation choices. Evolution of dynamic programming into Markov decision processes and reinforcement learning.

The course provides an introduction to circuit analysis and design for mechatronics applications. The focus is on building a working knowledge of: (1) op-amp circuits, (2) step response, steady-state response, and frequency response, (3) passive and active filter design, and (4) applications of the above to mechatronics systems, including sensors and instrumentation. The course will continue with a study of the fundamental behaviour and specific applications of the major semiconductor devices, including (5) diodes and (6) field effect transistors. Additional ‘design assignments' will require students to design real-world viable circuits for mechatronics applications, and laboratory experiments will present additional applications for all circuits being studied.

This course focuses on the integration of the results from earlier operations research courses and an assessment of the different methods with regard to typical applications. The course is taught using the case method. Students are expected to analyze cases based on real applications on their own, in small groups and during lecture sessions, and solve them using commercial software packages.

This course showcases the impact of analytics focusing on real world examples and case studies. Particular focus on decision analytics, where data and models are combined to ultimately improve decision-making. Methods include: linear and logistic regression, classification and regression trees, clustering, linear and integer optimization. Application areas include: healthcare, business, sports, manufacturing, finance, transportation, public sector.

Introduction to Artificial Intelligence. Search. Constraint Satisfaction. Propositional and First-order Logic Knowledge Representation. Representing Uncertainty (Bayesian networks). Rationality and (Sequential) Decision Making under Uncertainty. Reinforcement Learning. Weak and Strong AI, AI as Engineering, Ethics and Safety in AI.

Intro to Machine Learning, Hypothesis Spaces, Inductive Bias. Supervised Learning: Linear and Logistic Regression. Cross Validation (CV). Support Vector Machines (SVMs) and Regression. Empirical Risk Minimization and Regularization. Unsupervised Learning: Clustering and PCA. Decision Trees, Ensembles and Random Forest. Neural Net Fundamentals. Engineering Design considerations for Deployment: Explainability, Interpretability, Bias and Fairness, Accountability, Ethics, Feedback Loops, and Technical Debt.

This course provides a background in the fundamental areas in financial engineering including relevant concepts from financial economics. Major topics include interest rate theory, fixed income securities, bond portfolio construction term structure of interest rates, mean-variance optimization theory, the Capital Asset Pricing Model (CAPM), arbitrage pricing theory (APT), forwards and futures, and introduction to option pricing and structured finance.

This course deals with the formulation of optimization models for the design and operation of systems that produce goods and services, and the solution of such problems with mathematical programming methods, including linear programming: the simplex method, sensitivity analysis, duality, the revised simplex, column generation, Dantzig-Wolfe decomposition and linear programming with recourse; minimum cost network flows; dynamic programming; integer programming; non-linear programming models.

This course deals with the formulation of optimization models for the design and selection of an optimal investment portfolio. Topics include Risk Management, Mean Variance Analysis, Models for Fixed Income, Scenario Optimization, Dynamic Portfolio Optimization with Stochastic Programming, Index Funds, Designing Financial Products, and Scenario Generation. These concepts are also applied to International Asset Allocation, Corporate Bond Portfolios and Insurance Policies with Guarantees.

Fundamental concepts of vibration of mechanical systems. Free vibration single degree of freedom systems. Various types of damping. Forced vibrations. Vibration measuring instruments. Steady state and transient vibrations. Vibration of multi-degree of freedom systems. Vibration isolation. Modal analysis. Lagrange equations and Hamilton's principle. Vibration of continuous systems. Special topics.

Analysis of stability, transient and steady state characteristics of dynamic systems. Characteristics of linear feedback systems. Design of control laws using the root locus method, frequency response methods and state space methods. Digital control systems. Application examples.

This course covers the basic principles of the neutronic design and analysis of nuclear fission reactors with a focus on Generation IV nuclear systems. Topics include radioactivity, neutron interactions with matter, neutron diffusion and moderation, the fission chain reaction, the critical reactor equation, reactivity effects and reactor kinetics. Multigroup neutron diffusion calculations are demonstrated using fast-spectrum reactor designs.

This course covers the basic principles of the thermo-mechanical design and analysis of nuclear power reactors. Topics include reactor heat generation and removal, nuclear materials, diffusion of heat in fuel elements, thermal and mechanical stresses in fuel and reactor components, single-phase and two-phase fluid mechanics and heat transport in nuclear reactors, and core thermo-mechanical design.

Finite Element Method (FEM) is a very powerful numerical tool that has a wide range of applications in a multitude of engineering disciplines; such as mechanical, aerospace, automotive, locomotive, nuclear, geotechnical, bioengineering, metallurgical and chemical engineering. Typical applications include: design optimisation, steady and transient thermal analysis/stress analysis, wave propagation, natural frequencies, mode shapes, crashworthiness analysis, nuclear reactor containment, dynamic analysis of motors, manufacturing process simulation, failure analysis, to name a few. The focus of this course is to provide seniors and graduate students with a fundamental understanding of the principles upon which FEM is based, how to correctly apply it to real engineering problems using a commercial code. Specifically, participants will learn the principles governing model generation, discretization of a continuum, element selection, applying the loads and the constraints to real world problems. Participants will also learn how to scrutinize their model predictions, and avoid the pitfalls of this essential design tool.