Program Overview

Introduction to Data Driven Business Analytics

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

Summary

This course covers the following topics:

• Supervised learning
  • Linear Regression
  • Gradient Descent and Stochastic Gradient Descent
  • Multiclass Classification
  • Support Vector Machines
  • Decision Tree and Random Forest
• Unsupervised learning
  • k-means
  • Principal Component Analysis
• Deep Learning
  • Deep Neural Networks
  • Back propagation
• Time series
  • ARIMA models
  • Granger causality
• Quantitative Risk Management
  • Risk Measures: Value at Risk and Expected Shortfall
  • Statistical Estimation and Risk Measurement
• Portfolio Optimization
  • Mean-Variance Portfolio Optimization
• Statistical Learning for Finance
  • Shrinkage, Ridge Regression, LASSO and Dimension Reduction
  • Predicting Financial Returns

Keywords

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

Learning Prerequisites

• 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 exercises.

Expected Student Activities

Attending lectures and working on homework and projects.

Assessment Methods

Three homeworks (33.% each)

Supervision

• Office hours: Yes
• Assistants: Yes
• Forum: Yes

Resources

In the Programs

• Humanities and Social Sciences Program
  • Bachelor semester 6
  • Semester: Spring
  • Number of places: 80
  • Exam form: During the semester (summer session)
  • Subject examined: Introduction to data driven business analytics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Type: mandatory

Course Details

• Teacher: Aviolat Frédéric Jean-Michel
• Language: English
• Remark: Une seule inscription à un cours SHS+MGT autorisée. En cas d'inscriptions multiples elles seront toutes supprimées sans notification
• Credits: 2 credits

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