MGT-302 / 2 credits

Teacher(s): Kiyavash Negar, Ulrych Urban

Language: English

Remark: Une seule inscription à un cours SHS+MGT autorisée. En cas d'inscriptions multiples elles seront toutes supprimées sans notification

## Summary

This course focuses on on methods and algorithms needed to apply machine learning with an emphasis on applications in business analytics.

## Content

The following topics will be covered in the course:

1. Supervised learning

• Linear Regression
• Multiclass Classification
• K-NN
• Support Vector Machines
• Decision Tree and Random Forest

2. Unsupervised learning

• Regularization and Model Selection
• Cross Validation
• PCA

3. Deep Learning

• Deep Neural Networks
• Back propagation

4. Graphical models

• Bayesian networks
• Inference and structure learning

5. Causal inference in time series

• Granger causality
• Directed information Graphs

6. Quantitative Risk Management

• Risk Measures: Value at Risk and Expected Shortfall
• Statistical Estimation and Risk Measurement

7. Statistical Learning for Finance

• Shrinkage, Ridge Regression, LASSO and Dimension Reduction
• Predicting Financial Returns

## Keywords

machine learning, causal inference, time series, quantitative risk management

## Required courses

A course in basic probability theory

A course in basic linear algebra

Calculus

Familiarity with Python or Matlab

## Important concepts to start the course

Students should be familiar with basic concepts of probability theory, calculus, linear algebra, and programming.

## Learning Outcomes

By the end of the course, the student must be able to:

• Formulate supervised and unsupervised learning problems and apply it to data

## Transversal skills

• Assess one's own level of skill acquisition, and plan their on-going learning goals.

## Teaching methods

Formal teaching interlaced with practical exercices.

## Expected student activities

Attending lectures and working on homework and projects.

## Assessment methods

Three homeworks (33.33333333% each)

## Supervision

 Office hours Yes Assistants Yes Forum No

## In the programs

• Semester: Spring
• Number of places: 80
• Exam form: During the semester (summer session)
• Subject examined: Data driven business analytics
• Lecture: 2 Hour(s) per week x 14 weeks
• Type: mandatory

## Related courses

Results from graphsearch.epfl.ch.