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EnIESEG School of Management ( IÉSEG )

Class code :



Level Year Period Language of instruction 
Academic responsibilityA.RUBESAM


Basic statistics and probability
Econometrics (student must be very familiar with linear regression))
Basics in R.

Learning outcomes

At the end of the course, the student should be able to:
1. Describe how machine learning is used in general and in the field of Finance.
2. Compare and explain methods of machine learning such as supervised and unsupervised learning, tree-based methods and dimension reduction methods.
3. Implement several machine learning methods using the software R.

Course description

This is a technical course for students looking to deepen their knowledge in quantitative methods applied to finance. The course aims to cover the following:
- what is machine learning: understanding data and making predictions
- supervised vs unsupervised learning
- clasification versus regression problems
- assessing model accuracy
- trade-off between model interpretability and forecasting accuracy
- resampling methods: cross-validation and bootstrap
- regularization in linear models: LASSO, Ridge Regression
- tree-based methods, boosting, bagging and random forests
- dimension reductions techniques

Class type

Class structure

Type of courseNumbers of hoursComments
Independent study
Group Project8,00   Students will analyze a case study in groups and provide a final report.
Estimated personal workload14,00  
Independent work
Reference manual 's readings12,00   Time to study textbook.
Face to face
Interactive class16,00   Intensive course with access to computer with software R
Total student workload50,00  

Teaching methods

  • Case study
  • Project work


Assessment will be made through a case study where students should implement machine learning methods in R to solve a practical problem.
If time permits, daily quizzes may be applied.

Type of controlDurationNumberPercentage break-down
Continuous assessment
Case study8,00170,00
TOTAL     100,00

Recommended reading

  • James, Witten, Hastie and Tbishirani, 2017. An Introduction to Statistical Learning with Applications in R. -

Internet resources

* This information is non-binding and can be subject to change
Vidéo : Un campus à vivre
Notre chaîne Youtube