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Machine Learning In Business Analytics

  • Teacher(s):   M.Boldi  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2020-2021, 2.0h. course + 2.0h exercices (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

    Master of Science (MSc) in Management, Orientation Marketing

    Master of Science (MSc) in Management, Orientation Business Analytics
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Upon completion of that course the students will be able to

  • Select and apply rigorous machine learning methods to the cases covered during the course,
  • Use R to make these applications,
  • Analyze and interpret machine learning method results when applied to the cases covered during the course.


This course presents several machine learning techniques in business and management contexts. The list of topics is meant to cover mainly supervised methods of classification and prediction although unsupervised methods are also seen.

Below is a tentative lists of topics. It will be adapted according to the pace of the class

  • Classification: nearest neighbors, logistic regression, classification trees, naive Bayes classifiers, Support Vector Machine.
  • Regression: linear regression, regression trees, GAM.
  • Data splitting: training/test sets, cross-validation, bootstrap.
  • Model selections: scores (MSE, Accuracy, ...)
  • Unsupervised learning: clustering, PCA, MDS.
  • Ensemble methods: random forests, bagging.

Exercises and theory are equally important for the success of the class. A significant part of the exercises will be done on the statistical computer program R.


No mandatory document.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Science & Business Media.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer Science & Business Media.

Kuhn, M. and Johnson, K. (2013) Appied Predictive Modeling. Springer Science & Business Media.


First attempt

Without exam (cf. terms)  

- One individual written exam: 2hrs, organized during the semester.

- One applied project: individual or in groups, depending on the number of participants to the course. A final presentation will be organized during the semester.

Final grade = 0.3 exam + 0.7 project


Without exam (cf. terms)  

The retake is organized on the parts that were failed (written exam or/and project).

- Written exam: a retake exam of 2hrs will be organized.

- Project: a complement/correction to the project will be required.

Only the retaking grade will be concerned

Final grade = 0.4 exam + 0.6 project


- Final grade >= 4, no retake

- Final grade < 4

- with exam > 4, project < 4 => retake on the project, exam grade is kept

- with exam < 4, project >= 4 => retake on the exam, project grade is kept

- with both exam and project < 4 => retake on both

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