This class exists in these courses :
• IÉSEG > IESEG Degree - Programme Grande École > Semester 2 > 2,00 ECTS

Prerequisites

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

Teaching methods

• Case study
• Project work

Assessment

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.

Recommended reading

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

Internet resources

• https://www-bcf.usc.edu/~gareth/ISL/

• https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/