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Estimating ML-Models Financial Impact

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты

Преподаватель


Лакшина Валерия Владимировна

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 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
Course Contents

Course Contents

  • 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
Assessment Elements

Assessment Elements

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

Interim Assessment

  • 2021/2022 2nd 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.