A fundamental course that provides a grounding in nuclear engineering. Subject areas covered include overview of atomic-nuclear physics, nuclear materials, nuclear reactor physics; nuclear thermal hydraulics and power generation systems; nuclear corrosion chemistry; nuclear structural requirements; non-destructive testing-evaluation; and nuclear systems-operations and AI.

The various roles of a practicing engineer in industry and society will be presented through a series of seminars. The lecturers will include practicing engineers from local companies and consulting firms and representatives from professional and technical societies.

Focusing on a one-year-long research thesis, this course requires a student to work on a research project under the supervision of an academic staff member integrating concepts learned from across our curriculum to plan and execute a project resulting in a draft journal manuscript. Skills and concepts applied may include hands-on laboratory techniques, prototyping, computer simulations, and validation of simulation results. This approach requires students to integrate fundamental concepts from throughout materials science in development of a detailed, iterative literature review, gap analysis, research hypothesis/objectives, design of experiments, execution, and management of project plan. The research project scope should allow continuation into a MASc degree in the same topical area if desired. Significant time is allocated to individual meetings and consultation with the supervising professor to address the unique needs of each project. Students will apply design methodology learned from APS112, MSE294/MSE295 and MSE396/MSE397 to their research project. The course concludes with a formal oral presentation and a journal ready manuscript.

Focusing on a one-year-long capstone design, students work in teams on a design project under the supervision of a designated faculty member integrating concepts learned from across our curriculum to plan and execute a novel product or process design resulting in a final response to a “Request for Proposals” brief. This approach requires students to integrate fundamental concepts from materials science in the development of a detailed product design that includes five elements: a technical design, an economic assessment and business plan, safety analysis of the design, education and outreach to the community, and an environmental audit. Design projects belong to one of four theme areas: energy systems, materials processing, advanced manufacturing, or biomaterials. The course concludes with a formal oral presentation, video, and a final written report. MSE498 is the mandatory capstone course for all MSE students.

This course is designed to provide an integrated approach to composite materials design, and provide a strong foundation for further studies and research on these materials. Topics include: structure, processing, and properties of composite materials; design of fillers reinforcements and matrices reinforcements, reinforcement forms, nanocomposites systems, manufacturing processes, testing and properties, micro and macromechanics modeling of composite systems; and new applications of composites in various sectors.

An introduction to microeconomics, for application in public policy analysis. Designed specifically for students with training in calculus and linear algebra, and who are pursuing a certificate in public policy, the course will explore preference and choice, classical demand theory and the utility maximization problem as well as expenditure minimization problem, welfare evaluation of economic changes, regression analysis and ordinary least squares.

Knowledge of how governmental and non-governmental institutions work is essential to the study and development of public policy. This course will examine the formation, consequences and dynamics of institutions – from legislatures and courts to militaries and interest groups – in both democratic and authoritarian societies. We will also consider how institutions inform the relationship between individuals and the state, and how these social structures are instruments of policy implementation.

This course introduces students to the field of public policy - the means by which governments respond to social issues – and considers both why and how governments respond in these ways. To that end, we’ll examine the policy cycle, including how policy is proposed, made and reformed, as well as the role of regulation. And we’ll explore both theories of public policy and case studies of policy-making in action.

Mechanics forms the basic background for the understanding of physics. This course on Classical, or Newtonian mechanics, considers the interactions which influence motion. These interactions are described in terms of the concepts of force, momentum and energy. Initially the focus is on the mechanics of a single particle, considering its motion in a particular frame of reference, and transformations between reference frames. Then the dynamics of systems of particles is examined.

The first half of the semester will give an introduction to the basic ideas of classical oscillations and waves. Topics include simple harmonic motion, forced and damped harmonic motion, coupled oscillations, normal modes, the wave equation, travelling waves and reflection and transmission at interfaces. The second half of the semester will first give an introduction to Einstein's special relativity, including evidence for the frame-independence of the speed of light, time dilation, length contraction, causality, and the relativistic connection between energy and momentum. Then we will follow the historical development of quantum mechanics with the photo-electric and Compton effects, the Bohr atom, wave-particle duality, leading to Schrödinger's equation and wave functions with a discussion of their general properties and probabilistic interpretation.

The first half of the semester will continue with the development of quantum mechanics. Topics will include Shrödinger's wave mechanics, tunneling, bound states in potential wells, the quantum oscillator, and atomic spectra. The second half of the semester will give an introduction to the basic ideas of classical statistical mechanics and radiation, with applications to experimental physics. Topics will include Boltzmann's interpretation of entropy, Maxwell-Boltzman statistics, energy equipartition, the perfect gas laws, and blackbody radiation.

Experiments in this course are designed to form a bridge to current experimental research. A wide range of experiments are available using contemporary techniques and equipment. In addition to the standard set of experiments a limited number of research projects are also available. Many of the experiments can be carried out with a focus on instrumentation.

Experiments in this course are designed to form a bridge to current experimental research. A wide range of experiments are available using contemporary techniques and equipment. In addition to the standard set of experiments, a limited number of research projects may be available. This laboratory is a continuation of PHY327H1.

Individual and small group instruction in either solo instrumental or chamber music settings. Classical music or jazz. Auditions required.

The course is intended to provide an introduction and a very interdisciplinary experience to robotics. The structure of the course is modular and reflects the perception-control-action paradigm of robotics. The course, however, aims for breadth, covering an introduction to the key aspects of general robotic systems, rather than depth, which is available in later more advanced courses. Applications addressed include robotics in space, autonomous terrestrial exploration, biomedical applications such as surgery and assistive robots, and personal robotics. The course culminates in a hardware project centered on robot integration.

The course addresses advanced mathematical concepts particularly relevant for robotics. The mathematical tools covered in this course are fundamental for understanding, analyzing, and designing robotics algorithms that solve tasks such as robot path planning, robot vision, robot control and robot learning. Topics include complex analysis, optimization techniques, signals and filtering, advanced probability theory, and numerical methods. Concepts will be studied in a mathematically rigorous way but will be motivated with robotics examples throughout the course.

An introduction to the fundamental principles of artificial intelligence from a mathematical perspective. The course will trace the historical development of AI and describe key results in the field. Topics include the philosophy of AI, search methods in problem solving, knowledge representation and reasoning, logic, planning, and learning paradigms. A portion of the course will focus on ethical AI, embodied AI, and on the quest for artificial general intelligence.

This course will introduce students to the topic of machine learning, which is key to the design of intelligent systems and gaining actionable insights from datasets that arise in computational science and engineering. The course will cover the theoretical foundations of this topic as well as computational aspects of algorithms for unsupervised and supervised learning. The topics to be covered include: The learning problem, clustering and k-means, principal component analysis, linear regression and classification, generalized linear models, bias-variance tradeoff, regularization methods, maximum likelihood estimation, kernel methods, the representer theorem, radial basis functions, support vector machines for regression and classification, an introduction to the theory of generalization, feedforward neural networks, stochastic gradient descent, ensemble learning, model selection and validation.

The Robotics Capstone Design course is structured to provide students with an opportunity to integrate and apply the technical knowledge gained throughout their degree program toward the solution of a challenging real-world robotics problem. During the half-year course, students work in small teams and have considerable freedom to explore the design space while developing a complete robotic hardware and software system. The challenge task incorporates all aspects of the "sense-plan-act" robot design paradigm, with designs assessed based on engineering quality and performance relative to a series of benchmarks. In addition, each student completes a critical reflection on their team's performance and the evolution of their experience with design during their undergraduate program. Students are supported by a teaching team comprised of domain experts.

An introduction to aspects of computer vision specifically relevant to robotics applications. Topics include the geometry of image formation, image processing operations, camera models and calibration methods, image feature detection and matching, stereo vision, structure from motion and 3D reconstruction. Discussion of the growing role of machine learning and deep neural networks in robotic vision, for tasks such as segmentation, object detection, and tracking. The course includes case studies of several successful robotic vision systems.

The course addresses fundamentals of mobile robotics and sensor-based perception for applications such as space exploration, search and rescue, mining, self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, etc. Topics include sensors and their principles, state estimation, computer vision, control architectures, localization, mapping, planning, path tracking, and software frameworks. Laboratories will be conducted using both simulations and hardware kits.

Describes important fixed income securities and markets. The course emphasizes traditional bond and term structure concepts crucial to understand the securities traded in these markets. Students are required to work in the Rotman Financial Research & Trading Lab to solve the assigned problems using real time data. Not eligible for CR/NCR option. Contact Rotman Commerce for details.

This course examines the ways in which risks are quantified and managed by financial institutions. The principal risks considered include market risk, credit risk and operational risk. The course also covers the evolution of bank regulation and the regulatory limits on risk taking. Not eligible for CR/NCR option. Contact Rotman Commerce for details.

This course will use finance theory applied with Excel applications to understand potential returns and risks inherent in particular investment/trading strategies. Learning-by-doing will be facilitated by simulation-based Rotman Interactive Trader cases focused on particular risks. This training will be analogous to using a flight simulator for learning to fly. Not eligible for CR/NCR option. Contact Rotman Commerce for details.

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

An overview of probability from a non-measure theoretic point of view. Random variables/vectors; independence, conditional expectation/probability and consequences. Various types of convergence leading to proofs of the major theorems in basic probability. An introduction to simple stochastic processes such as Poisson and branching processes.

Programming in an interactive statistical environment. Generating random variates and evaluating statistical methods by simulation. Algorithms for linear models, maximum likelihood estimation, and Bayesian inference. Statistical algorithms such as the Kalman filter and the EM algorithm. Graphical display of data.

Discrete and continuous time processes with an emphasis on Markov, Gaussian and renewal processes. Martingales and further limit theorems. A variety of applications taken from some of the following areas are discussed in the context of stochastic modeling: Information Theory, Quantum Mechanics, Statistical Analyses of Stochastic Processes, Population Growth Models, Reliability, Queuing Models, Stochastic Calculus, Simulation (Monte Carlo Methods).

Complementary Studies elective

Entrepreneurship is the practice of identifying, creating, and capturing value – whether by launching a new venture or driving innovation within an existing organization. Engineering students will be introduced to the entrepreneurial mindset and toolkit through a structured, practical, and experience-based approach. Students will explore every stage of the entrepreneurial journey, from identifying unmet needs and designing value propositions to building business models, pitching ideas, and understanding the real-world mechanics of marketing, sales, finance, and leadership.

Topics include:
• Value creation, strategic thinking, and positioning
• Market research, innovation, and business model design
• The 4Ps of marketing, segmentation, targeting, and branding
• Organizational behavior, teamwork, and leadership
• Accounting, finance, and legal for early-stage ventures
• Sales, persuasion, and behavioral decision-making
• Pitching, scaling, and storytelling

Guest lectures from real entrepreneurs will be brought in and the work will be grounded in real-world cases and hands-on projects. No prior business experience is required.

This is the first of two complementary entrepreneurship courses (followed by TEP432) designed to help engineering students apply their problem-solving skills to the creation of ventures, products, and ideas that matter.

Humanities and Social Science elective

An examination of representations of science/scientists in theatre. Reading and/or viewing of works by contemporary playwrights and related materials on science and culture. Critical essays; in-class discussion and scene study.