Teaching Interests

My teaching interests span applications of machine learning to finance, asset pricing, macroeconomics, investments, and fixed income at any level from introductory undergraduate through advanced master’s.

Working at the nexus of computational methods and finance, I particularly enjoy bridging this gap for others: teaching finance fundamentals to technical audiences and technical methods to advanced finance and management students. I focus on integrating economic theory with mathematical modeling and numerical methods, emphasizing a blend of intuition and practicality to provide students with both theoretical foundations and real-world quantitative skills.

More recently, I am interested in teaching how to use AI tools responsibly and effectively for finance and coding workflows.

Current Courses

RSM 336 · University of Toronto · Commerce · Undergraduate

Investments

This course provides an introduction to financial theories and analytical tools for making investment decisions and for understanding how prices are determined for stocks and bonds. The course covers a broad range of topics including asset allocation, technical analysis, fundamental analysis, anomalies, and bond portfolio management.

RSM 338 · University of Toronto · Commerce · Undergraduate

Applications of Machine Learning in Finance

This course provides an overview of the basic tools in data analysis and machine learning, with emphasis on their applications in finance. Data analysis and machine learning play an important role in FinTech. Individual investors and financial institutions who are able to leverage these new tools and technology will have a significant advantage. This course discusses these new opportunities and challenges. It seeks to equip students with these highly coveted skills in the market.

Course site and notes: rsm338.kevinpmott.com.

RSM 8341 · University of Toronto · Master of Management Analytics · Master's

Analytical Methods in Finance

This course covers financial institutions, financial markets, and the management of different types of risk. It explains the types of funds that are created as investment vehicles and how measures such as value at risk and expected shortfall are used. It covers market risk, credit risk, operational risk, liquidity risk, and model risk and discusses the way the financial landscape is changing. The course will include a major NLP case study.

JRE 300 · University of Toronto · Engineering · Undergraduate

Fundamentals of Accounting and Finance

Introduces a brief overview of essential concepts in accounting and corporate finance. The first part of the course covers the fundamentals of accounting. We start by exploring the basic language of accounting and the fundamental concepts of financial reporting. Students learn to read and analyze basic financial statements including the statements of financial position, comprehensive income, changes in equity, and cash flows. We then introduce key management accounting concepts and explore various methods of costing for decision-making. The second part of the course covers the fundamentals of corporate finance. In the second half, students will learn how to make financial projections and how to value complex investment opportunities. Following this, students learn various techniques for controlling risk and how to determine the appropriate cost of capital. Finally, the course considers issues in cash flow management and overviews project valuation as it relates to corporate mergers.

University of Toronto · Rotman master's programs · Master's

Python for Business

A required prerequisite for the Master of Financial Risk Management (MFRM) and Master of Management Analytics (MMA), and optional for students in all other Rotman master’s programs. Delivered asynchronously, professionally recorded.

Python has become the industry standard coding language for a large variety of business tasks that you might encounter in your career after Rotman. This pre-course is meant to get you up to speed on Python in its modern state, with a business-first mindset: we encounter plausible problems, then uncover the convenient tools Python has to speed up our work. It also covers the companion tools that have become standard in any professional workflow, including git and GitHub, with GitHub Classroom used for lecture materials and assignments. A full module is devoted to using AI responsibly, and to adjusting our AI strategy based on the kind of task at hand.

Past Courses

Principles of Macroeconomics

73-103 · Tepper School of Business, Carnegie Mellon University · Undergraduate

Workshops

Managing and Leading with AI

Rotman School of Management, University of Toronto · Full-Time MBA Orientation AI Camp · 2026

This session introduces how to use AI tools efficiently contingent on the task at hand. Some tasks are basic and easily verifiable while others are complex and require deliberate decomposition. We’ll introduce a task modeling framework to understand how to use AI tools appropriately and efficiently in a variety of plausible business settings.

Available in HTML.

Creating Teaching Content with AI Tools as Writing Assistants

Rotman School of Management, University of Toronto · Community of Practice on AI in Teaching and Learning · 2025

I explain a flexible workflow framework for creating custom course materials using AI assistants as writing tools. The approach implements an iterative content generation pipeline where instructors retain control of pedagogical structure while delegating drafting, formatting, and iteration to AI. This editor-first methodology leverages modern language models to efficiently produce lecture slides, detailed notes, and practice materials tailored to specific student populations.

The flexible framework allows instructors to easily adapt the methodology to their own courses by customizing student level, examples, technical depth, and exposition style through LaTeX templates and AI configuration files.

Available on GitHub, hosted by Rotman FinHub.

Advanced Microsoft Excel Workshop

Tepper School of Business, Carnegie Mellon University · MBA · 2021–2023

Public Teaching Materials

Applications of Machine Learning in Finance

RSM 338 · Course notes and lecture materials

Available at rsm338.kevinpmott.com.

Linear Algebra for Quantitative Finance

Open-access reference

Students often hit a ceiling when they start to learn more technical skills such as analytics and machine learning: why does linear algebra show up everywhere in data work? As AI reduces the barriers to entry and enables people to start coding earlier, making sure you have the right base of mathematical and quantitative skills is more important than ever.

The notes skip geometric abstractions entirely and instead take a grounded, computational approach: what do these operations actually do to numbers, and how do you write more efficient code by vectorizing? The reference covers vectors, matrices, determinants, rank, and inverses with matched Python implementations throughout, building up to linear regression and a worked example in portfolio selection.

The main idea throughout: matrices are tools that let us efficiently batch linear operations.

Available in HTML and PDF.

Awards

Rotman School of Management, University of Toronto · 2025

Rotman Teaching Award