After an introductory review of the techniques most commonly used to evaluate investment portfolios and investment managers and an overview of the theoretical foundations of modern finance, this course will, through a combination of lectures, readings, real case studies and hands-on exercises, enable students to learn how to use — in real time and under real constraints — the five main algorithmic trading and portfolio management systems developed by the instructors to manage their own clients' investment portfolios in their professional private practice. After completing this course the participants should be able to manage basic Stocks and ETF portfolios as well as trading currency pairs and basic derivatives portfolios of Credit & Debit Spreads, by using time-tested "value" and "momentum" strategies, statistical-arbitrage pairs-trading techniques and covered options algorithms, all coded in the python programs developed by the instructors to that end. Students will also be able to manage the risk of any basic investment portfolio using index-option's hedging and/or market breadth-based algorithms, and to apply the best known tests to evaluate the back-testing results of different trading systems. As collateral benefits of this course, participants will be exposed to the basics of python in finance — as they learn how to calibrate the trading software shared by the instructors — as well as to basic equity valuation methods, basic portfolio optimization methods and basic bond and derivative pricing methods. Participants will be also exposed through case studies to the portfolio management strategies of some of the most important contemporary portfolio managers and apply digested versions of their techniques to basic portfolios under real market constraints. In the long run, after having assimilated and tested what they've learned in this course, students should be able to assemble general portfolio management strategies well adapted to their own risk/return profiles. For more details please go to the Course Layout section of the Syllabus.

In this course we'll give an overview of several applications of machine learning to stock market forecasting (including high frequency trading), beginning with regressions, two "shallow" machine learning models (Support Vector Machines and basic Neural Networks) and ending with a deep learning model (Long Short Term Memory Networks). Each model is discussed in detail as to what input variables and what architecture is used (rationale), how the model's learning progress is evaluated and how machine learning scientists and stock market traders evaluate the model's final performance, so that by the end of the course, the students should be able to identify the main features of a machine learning model for stock market forecasting and to evaluate if it is likely to be useful and if it is structured efficiently in terms of inputs and outputs.

The participant should be familiar with the foundations of statistics, the basics of logistic regressions (desirable), and basic linear algebra (desirable); however, since our course intends to be self-contained, we will provide a review of these concepts as needed. As all the examples of our course come from finance, some familiarity with the Capital Markets and the basic financial concepts is required. A basic knowledge of Python or some other programming language (MatLab, R) is needed, even though the objective of the course is not to learn how to program (shallow and deep) machine learning models from scratch, but rather, to understand how they work and to learn how to adapt them to the particular needs of the user and to optimize their application to stock market forecasting. The math foundations of the basic machine learning models (regressions, neural networks, and support vector machines) will be discussed and followed by a panoramic view of the inputs that are most likely to provide valuable information for stock market forecasting. Standard benchmarking methods used in the industry will be also covered. Subsequently, a number of basic — already programmed — models will be discussed in detail and their performance evaluated.

This course focuses on the application of advanced artificial intelligence techniques to 21 solved case studies in stock trading and hedging strategies, portfolio construction, modelling options strategies, investor's modelling, financial news and building Robotic-Advisors. The advanced techniques include the incorporation of recently open-sourced libraries for the calculation and evaluation of financial indicators, the incorporation of custom functions for cross validation, evaluation and model selection, the reformulation of problems in terms of reinforcement learning, and the implementation in Python of reinforcement learning solutions and the analysis of situations where reinforcement learning fails.

This is a course on how to define, solve, and communicate the solution to a business problem using an engineering approach. The course was developed and is taught by successful Canadian entrepreneurs who imagined, built and later sold their successful businesses. The course materials, lectures, and assignments are based on our personal experiences and published best practices. The course and materials provides students with a toolkit for solving business problems, and through the course project an exercise in how to do it. The key to business success is focused execution of a great business plan.

The learning activities in this course will rely heavily on graduate-level programming projects. The topics covered include: 1) Python programming (basic structures -- tuples, lists, sets, dictionaries, Pythonic programming style, e.g., list comprehensions, common packages -- numpy, scipy, matplotlib, pandas, Jupyter/IPython notebooks, OOP design and polymorphism and how to make effective use of it). 2) Probability and statistics (basic distributions, expectations and Monte Carlo approximations, importance sampling, change of variables / Jacobian, ANOVA / confidence intervals). 3) Matrix representations and fundamental linear algebra operations (e.g., quadratic form and multivariate Gaussians, trace, inverse, SVD, matrix derivatives). 4) Basic algorithms and data structures (sorting and array search, graphs and trees). 5) Discrete math (basic combinatorics, basic discrete optimization, e.g., weighted set cover). 6) Continuous optimization (gradient descent and variants, convexity). 7) [Optional if time] Constrained optimization (linear programming, mixed integer linear programming) with a focus on problem formulation.

Reinforcement Learning (RL) is a systems-level Artificial Intelligence toolset; this course will provide the student with both a solid theoretical foundation and a strong practical understanding of the subject. RL enables autonomous agents to cope with poorly-characterized, novel environments by exploring the environment to gain knowledge about it, and to exploit this knowledge of the environment to act in a goal-directed manner. Although RL is positioned as one of three facets of Machine Learning, RL has far broader scope than the narrower tools of supervised and unsupervised learning. RL, being founded on agent design, has the goal of developing artificial intelligence schemes that can endow an agent with autonomy. This introduction, thus, will be presented within the motivating context of an overall AI system. There are three foundational RL tools we will cover (dynamic programming, Monte Carlo, Temporal-Difference Learning); we will also show how hybridizations of these foundational tools are employed to create production schemes. The student should leave the course with the ability to practically apply this AI toolset to novel problems.

This is a course on how to start and run a successful Canadian business that is profitable on day one, using a real start-up example. If your ambition is to be your own boss one day this is the course for you. The course was developed and is taught by successful Canadian entrepreneurs who imagined, built, and sold at least one of their successful businesses. The course and materials provides students with a toolkit for starting and running a successful business, and through the course project an exercise in how to do that.If you already have a business idea the course will assist you in making that business idea a success and if you don’t have an idea the course will teach you how to find and develop one of your own.

Insurance has enabled each major socioeconomic transition over the last 600 years, whether underwriting shipping in the early days of international trade, to the introduction of machines, to advances in healthcare to capital infrastructure development. In the same way and at a much smaller scale, it allows entrepreneurs to secure loans, households to survive the accidental loss of a critical asset like a house or car, and manufacturers to retool and shift production.

While each situation is a unique risk profile, there are four broad approaches to managing commercial risks. These range from the prescriptive practice most commonly associated with FMGlobal, to the incentivized actions such as FireSmart and Home Improvement, to generalized advice on risk and resilience such as the BOMA Canada guide, to a fully integrated corporate risk management regime. Assessing the risks for underwriting will follow either a quality assurance or quality control approach.

In this course, students will: Learn the risk transfer process and how it's applied to underwriting, credit enhancement and transition planning; Explore each approach to commercial risk management; Learn how an insurer can understand the risks involved and their financial transfer in an insured-insurer partnership; Learn an evidenced first principles approach known as risk-based assessment (RBA) and how to apply it; Examine the RBA process in greater detail, including its use of primary data collection using open sources through to the interpretation of physical and transition risks; Explore the effects of climate and technological change over successive risk horizons; Learn compliance requirements concerning climate risk and ESG reporting standards; and Learn how to adapt and apply RBA to transition planning, risk strategy development, and equity projection parameters.

Risk assessment of a sociotechnical system identifies hazards that can result in human, material or environmental losses, the likelihood of such hazardous events, and their consequences. Traditional methods rely on probabilistic techniques to identify the potential for an accident before it occurs. However, such approaches are limited in their ability to account for social and organizational factors, interactions between system components with feedbacks, the adaptation of an organization in a constantly changing environment, and human behavior. This project-based course combines theory and practice to present a system dynamics approach to risk management. Dynamic systems are modelled using the STELLA® programming language. (A preliminary syllabus with additional details is attached.)

MASc students from MIE, and MA students from Psychology who are enrolled in the specialization will be registered in this two session course for one academic year. This is a type I weekly seminar course. MASc students, who usually take two years to complete their program, may elect to attend a second year if they find the seminar helpful for research.

Presentations, workshops, and discussion will be used in the seminar course to introduce theoretical foundations, methods, and techniques related to PsychEng research. Topics may change from year to year and may include: hypothesis generation, concept and knowledge mapping, survey design, mixed methods, ethics approvals, facilitating workshops, proposal writing, and preparation of manuscripts.

Faculty will present their relevant research, and students will present their work at various points to obtain feedback, with more advanced students having increasing involvement. Students will deliver a seminar on their research topic during their first term, and after designing and carrying out one or more experiments with input from other seminar participants, they will present their research results in their second term.

This is the PhD-level version of a required course for the September 2017 launch of PsychEng Collaborative Specialization, that was created for MASc students from MIE, and MA students from Psychology. Students who are enrolled in the specialization will be registered in this two sessions of the course. This is a type I weekly seminar course. PhD students may elect to attend subsequent years if they find the seminar helpful for research.

Presentations, workshops, and discussion will be used in the seminar course to introduce theoretical foundations, methods, and techniques related to PsychEng research. Topics may change from year to year and may include: hypothesis generation, concept and knowledge mapping, survey design, mixed methods, ethics approvals, facilitating workshops, proposal writing, and preparation of manuscripts.

Faculty will present their relevant research, and students will present their work at various points to obtain feedback, with more advanced students having increasing involvement. Students will deliver a seminar on their research topic during their first term, and after designing and carrying out one or more experiments with input from other seminar participants, they will present their research results in their second term.

The PhD-level seminar course requires a higher level of participation than the master's level course, where PhD students will present more frequently and/or take a leadership role in seminar activities, such as the discussion of research papers.

This course presents an overview of the waterpower industry, beginning with its ecological and historical contex, how power stations work, and their major components. After a site tour, students will consider the major decision making factors in the design of a waterpower facility: policy and planning considerations, the business case, commercial markets, and risk management. With this, the finer details of power station design are introduced: turbine selection, electrical and control components, as well as how these components work. Further, students are introduced to operations and maintenance considerations, contract models, and dam safety. A course project will mirror these topics, as students evaluate the feasibility of a greenfield waterpower facility. With the support of industry professionals, students will work through the complexity and ambiguity of a development project.

Waterpower infrastructure is both ageing and being repurposed. This course looks at how the design of waterpower dams, structures, and equipment has been shaped by technological change over time. Students will learn to analyze the upgrade potential of an existing plant; review the tools and data available to understand site condition and to be aware of modernization scope for structures and equipment in the context of environmental, social, technical, and economic decision making. Through upgrading of aging infrastructure waterpower not only supports the electricity grid, but also plays a role in integrated water management around flood and drought mitigation, navigation, and recreations. They are long-term assets, in a world of short-term investment. This course exposes students to how waterpower facilities can be repurposed to suite contemporary needs. Student will be able to: To appreciate and assess the profile of existing hydro fleet both in Ontario, Canada and more broadly; To understand design considerations for hydro dams, structures and equipment as they evolved over time, including civil, mechanical, and electrical components and their interaction; To analyze upgrade potential of an existing plant, making key distinctions between power, energy, and dispatchability; To be aware of modernization scope for structures and equipment; To be able to recognize and articulate key safety hazards and concerns associated with renewal and construction work; To have studied how system failure has occurred in the past and to articulate some of the key vulnerabilities associated with hydro development.

Through a combination of lectures, guest talks, readings, and case studies, we will learn about the history and competing theories of international development, globalization, and foreign aid; major government, nongovernment, and multilateral actors in development; emerging models of development (social entrepreneurship, microfinance, risk capital approaches); classic diffusion of technology models that derive from anthropology, sociology, psychology, geography and migration studies; and the economic history that trace barriers to the use of innovations. This course will specifically focus on the impact on innovation as it applies to rural agricultural development, humanitarian engineering, and WASH.

The Institute for Multidisciplinary Design & Innovation (UT-IMDI) promotes opportunities for U of T Engineering students to participate in industry-sponsored projects, and in particular those that require a multidisciplinary team of students. UT-IMDI will source projects, but also welcomes projects sourced by faculty within the FASE. This course recognizes the involvement of an MEng student in a Multidisciplinary MEng Project.

In this course students will develop spatial skills relating two and three dimensional problems of design and developing sophisticated spatial strategies from elementary exercises. Students will be asked to turn abstract design questions into architectural environments. Finally, students will be introduced to the interaction between primary structural principals and form. The studio will introduce simple building programs and site relationships.

This is the second studio in the sequence of core graduate architecture design studios at the John H. Daniels Faculty of Architecture, Landscape, and Design. This studio hopes to be a concrete response to the Calls to Action outlined in the Wecheehetowin ‘Answering the Call’ University of Toronto – Truth and Reconciliation Commission of Canada 34 Calls to Action. This architecture design studio proposes four concepts for study: site, matter, ecology, and indigenous story work.

This course prompts students to develop a capacity and language to critically read and examine images and to evaluate an image's conceptual, technical, and political value as a layer of its aesthetic performance. 

In practice today, the architect is paid for drawings sets produced. However, to limit the role of drawings and models merely to instructions for others to follow is to miss a huge part of what the act of drawing brings to the genesis of architecture. Architecture is more than drawing buildings, it is also the conceptualization of what that building is and drawing and modelling act as generative and explorative devices beyond simply communication tools. When the means of creating the drawing are computational and algorithmic, the exercise of thinking with the tool gains new perspectives, grants opportunities and presents affordances.

The course will introduce and discuss computer-aided design (CAD), computational design tools, bottom-up and top-down computational design methods, simulation, and digital fabrication techniques. The course will make extensive use of the Rhinoceros CAD software and the visual programming language Grasshopper. The course will present issues of physical model making, the digital fabrication of models, and how digital technology is impacting the fabrication and assembly processes in the building industry.

This lecture course is the first part of a two-semester survey of architectural history and theory. Rather than attempting to be strictly chronological and exhaustively comprehensive, the two courses study the built environment through a series of carefully curated thematic modules. The aim is to introduce students to a range of case studies and analyze the many lenses through which one might conceptualize architecture as a method of history, as a critical practice, and as a form of politics. 

This course is the second of a two-semester survey of architectural history and theory. Rather than adopting chronology and survey as structuring frameworks, the two courses study the built environment through six thematic modules. The aim is to introduce students to a range of case studies through some of the many lenses through which one might understand architecture and its history as critical practices and political forms.

This is the first of seven technics and planning courses in the Master of Architecture program and explores technics in the broadest sense of the term from building materials and methods to intellectual processes underpinning architecture. The course examines how ecology, culture and technology converge within the warp and weft of architecture so that students are better able to contextualize their design thinking to responsibly address our common future. 

This course introduces the fundamental concepts behind modern building science, and applies these to the performance of materials and methods of construction for traditional and contemporary building systems. The course deals with building science theory, and its practical application. The increasing importance of building performance for architectural design will be reviewed in the context of changing codes, standards, and increasing building owner/occupant concern for the environmental implications of buildings. 

Introduction to the principals of structural analysis and design. The course focuses on structural systems and loading, including gravity and lateral load paths, basic construction methods and review of structural documents.