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.

This course builds upon the material introduced in Fluid Mechanics I and focuses on technical applications of fluid flow. Discussed topics include the pressure drop in pipe and channel flow networks, transient flow phenomena, external flows, performance characteristics of different pumps and turbines, systems of flow networks and flow machines, and an overview of modern flow measurement techniques. Lectures are complemented by laboratory experiments on topics such as pipe/channel networks, flow transients, and flow machines.

Introduction to Computer Integrated Manufacturing. Definitions, terminology. Organization of manufacturing systems. Introduction to NC machines. Introduction to robotics. Types of robot motion. Robot kinematics. Jacobians, singularities. Robot motion trajectories. Interpolation, spline fits. Robot joint control. Flexible manufacturing systems, justification. Robot cell design. Group technology. Design of group technology cell. Programmable logic controllers. Limited enrolment.

1. To enable deeper understanding and more flexible use of standard machine learning methods, through development of machine learning from an Optimization perspective.

2. To enable students to apply these machine learning methods to problems in finance and marketing, such as stock return forecasting, credit risk scoring, portfolio management, fraud detection and customer segmentation.

A half-year capstone design course in which students work in small teams to apply the engineering design, technical, and communication skills learned previously, while refining their skills in teamwork and project management. The course will take a "systems approach" to machine intelligence design, where students will identify, frame and design solutions to real-world problems in the field. Students will engage with industry partners, and work through a process that results in a functional prototype. The resulting designs are assessed on their engineering quality and design credibility. In addition, each student engages in individual critical reflection on their course activities, team performance, and on their growth as an engineering designer across their undergraduate program. Students are supported by a teaching team comprising both design and domain experts.

Injury biomechanics uses the principles of mechanical engineering to understand how injuries occur in various body regions and the main approaches to prevent them. In this course, we will review the injury mechanisms at the tissue level and the injury criteria for the lower extremities, upper extremities, head, neck, and trunk. Topics in injury prevention methods through safety devices and safely designing the equipment will be studied as well as engineering design considerations in treating a skeletal injury. The course also covers the computational (finite element analysis, and statistical analysis) and experimental (mechanical testing of crash test dummies, artificial bones, PMHS, and ex-vivo specimens) research methods used in injury and orthopedic biomechanics. Students will have the opportunity to apply their learning in forensic biomechanics case studies, and design and analysis of protective equipment.

Review (number systems, CPU architecture, instruction sets and subroutines); Interfacing Memory; Interfacing Techniques; Transistors and TTL/CMOS Logic; Mechanical Switches & LED Displays; Interfacing Analog, A/D & D/A Conversions; Stepper Motors & DC Motors; RISC Technology and Embedded Processors; DAS Systems; Embedded Microcontroller System Design; CPU-based Control.

Introduction to the application of the principles of mechanical engineering - principally solid mechanics and rheology - to living systems. Topics include cellular mechanics and hard and soft tissue mechanics, with consideration of both experimental approaches and analytical modelling. Applications of these topics to biomimetic and biomechanical design are emphasized through a major, integrative group project.

Products should be used as intended to be effective. Thus, a primary goal is to better align designer intention and user behavior. More specifically, sustainability-minded products should be technically efficient, but also support people to use them more sustainably. Finally, many products and systems nudge people to behave in ways contrary to the user's best interests. To address the above, the course focuses on design that increases intended product use, and pro-social / pro-environmental behaviors. For projects, students will develop, prototype and test concepts that aim to increase desired behaviors. Methods relevant to the design of all products include: identification of unmet/underserved user needs through lead users; roles of function and affordance in effective products; fixation and cognitive biases as obstacles to creativity; concept generation methods (e.g., Theory of Inventive Problem Solving (TRIZ/TIPS), use of stimuli and analogy); configuration design methods (e.g., design for transformation, manufacture, assembly, reuse, repair, and recycling).

Problem definition and formulation for optimization, optimization models, and selected algorithms in optimization. Design for Tolerancing, Design for Manufacturing, and Design for Assembly. State of the art Computer Aided Design packages are introduced with case studies. Emphasis is placed on gaining practical skills by solving realistic design problems.