Assistant Professor of Finance, Teaching Stream
Director of Teaching Innovation, FinHub: The Financial Innovation Lab
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.
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.
Available at rsm338.kevinpmott.com.
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.