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Regular version of the site

Estimating ML-Models Financial Impact

2021/2022
Academic Year
ENG
Instruction in English
3
ECTS credits
Delivered at:
Department of Economic Theory and Econometrics (Faculty of Economics)
Course type:
Elective course
When:
2 year, 2 module

Instructor

Course Syllabus

Abstract

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
Learning Objectives

Learning Objectives

  • The course is aimed at teaching how to estimate the expected financial results of a given ML model.
Expected Learning Outcomes

Expected Learning Outcomes

  • Learn principles of projects valuation which are also relevant for model implementation projects Learn the difference between NPV, IRR and PI
  • 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 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 about potential biases within historical data which can mislead your financial estimates Learn how metalearning can help to restore unobserved events
Course Contents

Course Contents

  • Project valuation: valuation metrics, planning and rules
    Model development and deployment is always a project. During this week, we will discuss project valuation. We will consider the general financial metrics such as Net Present Value, Internal Rate of Return, and others.
  • Model quality and decision making. Benefit curve
    During the second week, we will focus on decision-making based on model predictions and the relationship between model quality and financial benefit. We will discuss how to plot benefit curves using different threshold decisions and optimize the financial results using threshold tuning.
  • Estimating model risk discounts
    When a model is being used in the production environment for a long time, its quality can deteriorate. During this week, we will learn to calculate confidence intervals for model quality estimates and estimate potential negative financial effects.
  • A/B testing and financial result verification
    A/B testing is a great way to verify our expectations concerning the financial effects. During this week, we will discuss the principles of A/B testing, its design, as well as the ways to assess its outcomes.
  • Unobservable model errors, metalearning
    Imagine that our historical data is biased, and we cannot obtain any other data. During this week, we will discuss how to restore unobservable events by using such methods as reject inference and metalearning.
Assessment Elements

Assessment Elements

  • non-blocking Average of all quizzes
  • non-blocking Final test
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    The 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%
Bibliography

Bibliography

Recommended Core Bibliography

  • 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

Recommended Additional Bibliography

  • Bernard Marr, & Matt Ward. (2019). Artificial Intelligence in Practice : How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.