Strategy and Innovation

Ajay Agrawal

Peter Munk Professor of Entrepreneurship, University of Toronto, Rotman School of Management

This course provides an introduction to applied economics in the context of innovation and strategy that is relevant to entrepreneurship and early stage ventures. We workshop each venture and address questions such as: How can the 'lean' approach to startups be applied to your venture? Why might markets that are subject to increasing returns be likely to exhibit 'extreme competition' and what are the implications for your venture? How might your venture employ 'judo strategy' to exploit the normally advantageous size of large competitors? How might your venture be able to exploit the well-known 'innovator's dilemma' to compete with established competitors? To what extent are clever strategic tactics congruent with overall welfare for humankind? We will explore such questions through the lens of economic theory, apply the concepts in the context of case analyses, and discuss implications for corporate strategy.

Business, Government and the Global Economy

Marc Busch

Karl F. Landegger Professor of International Business Diplomacy at the Edmund A. Walsh School of Foreign Service, Georgetown University

This course offers an introduction to the political economy of international business. The motivation for the course is to explore the nonmarket strategies employed by firms to compete in international trade. Nonmarket strategy involves political, legal and economic efforts by firms to reshape the rules of competition. With the 'visible hand' of governments very much evident in today's global economy, nonmarket strategy plays a key role in supporting a firm's market strategy. The course surveys topics in international trade with an eye to the nonmarket challenges that firms confront in 'going global.' The course looks at issues ranging from protectionism to obligations under the World Trade Organization and the growing number of preferential trade agreements that often extend more stringent rules than the multilateral system.

Strategic Experimentation, Crowdfunding and the Blockchain

Christian Catalini

Assistant Professor of Technological Innovation, Entrepreneurship, and Strategic Management, Sloan School of Management, Massachusetts Institute of Technology

The objective of this course is to accelerate your progress in the program and expose you to the process of experimentation at the business, strategic and ecosystem level. We will start by taking the perspective of your potential early-adopters: is their current behaviour consistent with your assumptions about how your product or service can deliver value to them? Will your offering be compelling enough for them to change their behaviour? We will learn how to formulate testable and precise hypotheses, and experiments to test them. Next, we will discuss how different types of experimentation can ultimately help you scale your venture, and how you can develop a robust entrepreneurial strategy to maximize learning. From the theory we will then turn to the phenomena, and focus on two technologies that lower the cost of experimentation in the economy, opening new opportunities and challenges for entrepreneurs: crowdfunding and the Bitcoin blockchain.

Digital Market Strategy

Joshua Gans

Professor of Strategic Management, Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship, University of Toronto, Rotman School of Management

This course will examine the elements of digital strategy for entrepreneurs. Its focus will be on how to understand your competition, how to build on network effects, what the differences between a platform, disruptive, competitive and collaborative strategy are and when they should be used. It will also present the economics of app pricing; how to price an app, pricing options and innovations, freemium and two-sided markets.

Market Research: Data Collection and Analysis

Avi Goldfarb

Professor of Marketing, Rotman School of Management, University of Toronto

This course explores how to collect, analyze, and present market intelligence.  The purpose of the course is to prepare you to conduct proof-of-concept studies and to undertake effective experiments. We will cover five topics: (1) study design, (2) segmentation, (3) experiments, (4) interpreting correlations, and (5) communicating data. The underlying objective is to understand your target customers and assess whether their interests and behaviour are consistent with assumptions behind your business model.

Entrepreneurial Finance

Ramana Nanda

Associate Professor of Business Administration, Harvard Business School

This class examines the elements of financing a startup, focusing on technology-based startup ventures, and the early stages of company development. It addresses key questions which challenge all entrepreneurs: how much money can and should be raised; when should it be raised and from whom; what is a reasonable valuation of the company; and how funding should be structured. The course is primarily aimed to prepare students for these decisions as entrepreneurs but also examines issues from the perspective of venture capitalists, so that students can learn to effectively pitch their financing strategy to VC and Angel investors.

Economics of Entrepreneurship

Reza Satchu

Managing Partner, Alignvest Capital Management, Serial entrepreneur

This course focuses on the challenging and important economic issues associated with recognizing, growing and valuing new ventures. The course explores the analytical techniques needed to recognize emerging business opportunities; understand the various financing choices; apply valuation methodologies; develop a marketable business plan; manage growth in a rapidly evolving environment; and successfully monetize the value of a business. Students will develop a framework within which to analyze whether a business idea is worth pursuing and a methodology to enable them to apply financial economic principles in ways that add to the value of an entrepreneurial undertaking.

Machine Learning

Machine learning (ML) is a paradigm shift across many sectors because it proposes the automatic construction of artificially intelligent systems that are human-like in ability to perform and adapt. It has already made well-publicized breakthroughs in transforming massive amounts of unstructured data into commercially-relevant products and services. Accordingly, the major internet giants have made recent acquisitions and investments in this space as they expand their capacity in AI. A recent study by Bloomberg Beta estimated over 2,500 startup companies also working in AI and ML. 

This course will begin by introducing the core principles of machine learning. It will then focus on Deep Learning: techniques that learn multiple layers of representation. It will review core approaches for supervised learning: deep neural networks, backpropagation, and optimization methods. It will also review unsupervised learning techniques, including recent advances in deep generative models.

Computer Vision

A central challenge in automated visual reasoning is that of untangling the many factors of variation that explain an image or video: both nuisance factors (e.g. lighting, scale, camera angle) and variables of interest (e.g. person or object identity). Historically, practitioners relied on an engineered feature extraction pipeline, usually containing multiple stages of processing combined with simple machine learning techniques. Recently, Deep Learning methods have transformed the field producing winning entries to myriad competitions as well as industrial applications. 

This course will review the foundations of Deep Learning applied to vision including contemporary convolutional network architectures. Leading experts in the field will discuss the most relevant application areas, including object detection, structured prediction, large-scale classification and hardware acceleration, video, multi-modal and multi-task learning, and regression methods for localization. The course will also highlight the most frequently used practical development libraries and tools.

Natural Language Processing

Applications of Natural Language Processing (NLP) systems are everywhere: web search, translation services, and recommender systems, are only a few examples. Understanding language is not just the core to so many products and services, it is a critical component of building strong AI systems. NLP is one area which has been deeply affected by the recent advances in Deep Learning. This course will review NLP fundamentals as well as cutting-edge models fueled by DL. Topics include word embeddings, recurrent neural network models, encoder-decoder architectures, attention models, architectures with external memories, and multimodal learning, including image and video captioning systems.

Intelligence in Practice

This course explores practical aspects of Machine Learning in industrial settings. It will explore the interaction between software and hardware, specifically Graphics Processing Units (GPUs) and other hardware accelerators which have been critical to scaling up ML. It will cover best practices for ML developers, such as model search, debugging, and visualization. Short and long-term ethics and implications of strong AI will form a key discussion point. Machine Learning Systems have a larger system-level complexity than traditional software-based systems and thus have the potential to incur massive ongoing maintenance costs. The course will also explore the challenges and best practices of building and deploying large-scale ML systems.

Data Driven Decision Making

Mara Lederman

Director, Research Resources and Centres Associate Professor of Strategic Management

Mara does empirical research in the areas of Industrial Organization and Organizational Economics. At a broad level, Mara studies how companies compete and how they organize themselves for competitive advantage.  She is best known for her research on loyalty programs and vertical integration. Much of her work has been focused on the airline industry and she is recognized as an expert in this area. Her work has been published in the American Economic Review, the RAND Journal of Economics and the Review of Economics and Statistics, among others.  On the teaching side, Mara delivers courses on strategy, data analytics, and business problem-solving in Rotman’s MBA and Executive Education programs. 

Reinforcement Learning

Joelle Pineau

Head of Facebook AI Research Lab, Associate Professor of CS at McGill University

Technical Faculty