Program Overview

Machine Learning Methods in Econometrics

Course Overview

This course provides graduate students with a comprehensive understanding of the methods, theory, mathematics, and algorithms required to apply machine learning techniques in the business analytics domain. The course covers topics from machine learning, classical statistics, and data mining.

Course Content

The course includes the following topics:

• General Introduction
• Supervised Learning, Discriminative Algorithms:
  • Supervised Learning Concept
  • Linear Regression
  • Maximum Likelihood
  • Normal Equation Gradient Descent
  • Stochastic Gradient
  • SVRG
  • Linear Classification
  • Logistic Regression
  • Newton Method
• Generative Algorithms:
  • Multivariate Normal
  • Linear Discriminant Analysis
  • Naive Bayes
  • Laplacian Smoothing
  • Multiclass Classification
  • K-NN
  • Support Vector Machines and Kernel Methods:
    • Intuition
    • Geometric Margins
    • Optimal Margin Classifier
    • Lagrangian Duality
    • Soft-margin
    • Loss function
    • Stochastic Subgradient Method
• Unsupervised Learning:
  • PCA
  • Mixture Models
  • Bayesian Graphical Models
• Regularization and Model Selection
• Decision Tree and Random Forest:
  • Entropy
  • Bagging features
  • Bagging Samples
  • Random Forest
• Neural Network:
  • Concept
  • Deep Neural Network
  • Backpropagation Convolutional Neural Network
• Causal Inference:
  • Potential outcomes
  • DiD
  • CiC
  • IVs
• Applications to business analytics

Keywords

• Supervised and unsupervised learning
• Model selection
• Generative models
• Causality
• Cases and applications to business analytics

Learning Prerequisites

• A course in basic probability theory and linear algebra
• Ability to program in Python or Matlab

Recommended Courses

• Statistics

Important Concepts to Start the Course

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

Learning Outcomes

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

• Formulate supervised and unsupervised learning problems and apply them to data
• Analyze and apply generative models
• Explore and train basic neural networks and apply them to data
• Identify causal inference vs association and perform inference tasks on data

Transversal Skills

• Assess one's own level of skill acquisition and plan ongoing learning goals

Teaching Methods

Classical formal teaching interlaced with practical exercises

Expected Student Activities

Active participation in exercise sessions is essential

Assessment Methods

• 30% Homework
• 30% Midterm project
• 40% Final project

Supervision

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

Resources

• Moodle Link

In the Programs

• Management, Technology, and Entrepreneurship Master semester 2
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Machine learning methods in econometrics
  • Courses: 2 hours per week x 14 weeks
  • Exercises: 1 hour per week x 14 weeks
  • Type: Optional
• Management, Technology, and Entrepreneurship Master semester 4
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Machine learning methods in econometrics
  • Courses: 2 hours per week x 14 weeks
  • Exercises: 1 hour per week x 14 weeks
  • Type: Optional
• Financial Engineering Master semester 2
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Machine learning methods in econometrics
  • Courses: 2 hours per week x 14 weeks
  • Exercises: 1 hour per week x 14 weeks
  • Type: Optional
• Financial Engineering Master semester 4
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Machine learning methods in econometrics
  • Courses: 2 hours per week x 14 weeks
  • Exercises: 1 hour per week x 14 weeks
  • Type: Optional
• Financial Engineering minor Spring semester
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Machine learning methods in econometrics
  • Courses: 2 hours per week x 14 weeks
  • Exercises: 1 hour per week x 14 weeks
  • Type: Optional
• Management, Technology, and Entrepreneurship minor Spring semester
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Machine learning methods in econometrics
  • Courses: 2 hours per week x 14 weeks
  • Exercises: 1 hour per week x 14 weeks
  • Type: Optional

Reference Week

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