Estimating ML-Models Financial Impact
- The course is aimed at teaching how to estimate the expected financial results of a given ML model.
- Learn about potential biases within historical data which can mislead your financial estimates Learn how metalearning can help to restore unobserved events
- Learn about reasons and consequences of model risk Learn how to account for unexpected decrease in model quality with the help of confidence intervals
- Learn how to evaluate A/B test results with the help of hypotheses testing Learn how to properly design A/B tests
- Learn how to plot benefit curves which are similar to ROC curves but represent expected financial benefits Learn about different ways the decisions are made based on model predictions
- Learn principles of projects valuation which are also relevant for model implementation projects Learn the difference between NPV, IRR and PI
- Project valuation: valuation metrics, planning and rules
- Model quality and decision making. Benefit curve
- Estimating model risk discounts
- A/B testing and financial result verification
- Unobservable model errors, metalearning
- 2021/2022 2nd moduleThe assessment is based on the certificate for the online course. Score (10-point scale) Criterion 10 95 - 100 % 9 85 - 94 % 8 75-84% 7 65-74% 6 55-64% 5 45-54% 4 35-44% 3 25-34% 2 15-24% 1 less than 15%
- Cornwall, J. R., Vang, D. O., & Hartman, J. M. (2019). Entrepreneurial Financial Management : An Applied Approach (Vol. Fifth Edition). New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2237944
- Bernard Marr, & Matt Ward. (2019). Artificial Intelligence in Practice : How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.