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Machine Learning In Business Analytics

  • Teacher(s): M.Boldi
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Autumn Semester 2019-2020, 2.0h. course + 2.0h exercices (weekly average)
  •  séances
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Management, Orientation Marketing

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

    Master of Science (MSc) in Management, Orientation Business Analytics

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

[warning] This course syllabus is currently edited by the professor in charge. Please come back in a few days. --- For your information only, here is the old syllabus :

Objectives

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.

Contents

This course presents several machine learning techniques put in business and management contexts. The list of topics is meant to cover mainly supervised methods of classification and prediction. Comparisons of models will be seen in broad context. Some preliminary analysis techniques (e.g. dimension reductions) and specific topics (e.g. missing values) will be covered if time permits. 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.
  • Forecasting: regression, regression trees, MARS.
  • Model selections: scores (MSE, Accuracy, ...)
  • Dealing with missing data
  • Dimension reduction techniques (PCA)
  • Special topics: 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.

References

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.

Evaluation


 

First attempt


 
Exam:
Written 2h00 hours
Documentation:
Allowed with restrictions
Calculator:
Allowed
Evaluation:

One final exam: 100% of the final grade


 

Retake


 
Exam:
Written 2h00 hours
Documentation:
Allowed with restrictions
Calculator:
Allowed
Evaluation:

Retake exam: 100% of the final grade



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