Special Topics 2020 - 2021

The University of Toronto Mississauga Management (UTMM) offers a variety of special topics courses to complement your program. These courses discuss current trends and practices in business and are facilitated by Management faculty. Many programs offer the flexibility to allow these courses to count towards program completion. Be sure to check your program details.


This course is an introduction to the economics of blockchain technology and its applications to finance. We will discuss the functioning of blockchains; fundamentals of cryptography; zero-knowledge proofs; the economics of decentralized platforms; stablecoins and blockchain monetary policy; tokenomics, tokenization, and the usage of tokens in finance; decentralized banking applications; decentralized exchanges and crypto trading.
  • Professor Andreas Park / Term: Fall 2020


Through hands-on experience, you will learn the fundamentals of Python, which will serve as the primary programming language in this course. Python is widely used in many financial institutions today. Topics include high-frequency trade and quote data analysis, big data visualization techniques, empirical asset pricing and automatic portfolio optimization, as well as option pricing algorithms such as binomial trees and Monte Carlo simulation. No previous experience with programming is required.
  • Professor Marius Zoican / Term: Winter 2021


The course has two main objectives. First, it will introduce students to the emerging field of FinTech. We focus on two main technological innovations, blockchain technology and machine learning (which relates to artificial intelligence), that facilitate this transformation and that these FinTechs use. We will study the process of founding and financing of a FinTech startup. The second objective is to give students the opportunity to develop a viable FinTech product idea, based on a thorough analysis of the business models of two to three successful FinTech firms.
  • Professor Andreas Park / Term: Fall 2020


This course studies risk management from a holistic perspective for various institutions (i.e. non-financial and financial). Various risk categories will be considered such as cyber risk, operational risk, market risk, energy risk, technology risk, financial risk, etc. Leading edge cases and frameworks will be integrated into the course.
  • Professor Otto Yung / Term: Winter 2021


This course will introduce students to a diverse collection of big data
techniques. These techniques are often aimed at identifying and quantifying various structures in the data. Model validation and effective communication of model-based results will be stressed. The course will employ a “white-box” methodology, which emphasizes an understanding of the algorithmic and statistical model structures. The course will use Python to apply a number of different algorithms to real-world big data.
  • Professor Gerhard Trippen / Term: Winter 2021


In this course, students are introduced to a variety of models and techniques used in predictive analytics. These models include linear and nonlinear regression, classification algorithms, causal inference, and machine learning techniques such as random trees and reinforcement learning. The dual emphases of the class will be on: developing a conceptual understanding and interpretation of the models, and using hands-on applications to build experience and reinforce concepts. The programming exercises will take place in the popular R programming language.
  • Professor Andrew Steck / Term: Winter 2021