Why Machine Learning in Economics and Finance

PRELIMINARY OPEN DISCUSSION

Machine Learning: The Business Impact

The Power Of Machine Learning: The Business Impact On Real-Time Data (Retrieved August 2025)

“[…]impressively, the machine learning market—which brought in $15.44 billion in 2021—is expected to reach almost $210 billion by 2030, ballooning 38.8% annually.”

Machine Learning: Boosting Business Growth

The Power Of Machine Learning: The Business Impact On Real-Time Data (Retrieved August 2025)

“Machine learning reduces friction at every stage of a business, whether you’re coming up with new product ideas or getting the goods delivered to the client. It increases business efficiency, improves customer relationships and boosts sales.”

Some Examples of Machine Learning Tasks

Hands-On Machine Learning with R

• Predicting the likelihood of a patient returning to the hospital (readmission) within 30 days of discharge.

• Segmenting customers based on common attributes or purchasing behavior for targeted marketing.

• Predicting coupon redemption rates for a given marketing campaign.

• Predicting customer churn so an organization can perform preventative intervention.

Machine Learning (ML): MIT Sloan

ML Opinion

ML: American Economic Review

American Economic Review

ML: National Bureau of Economic Research

National Bureau of Economic Research: AI and Economy

Books Statistics/ML: Main Textbook

• An Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Books Statistics/ML: Good to Read

• The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman

Books Statistics/ML: Good to Read

• Computer Age Statistical Inference Bradley Efron and Trevor Hastie

More Books

TRY for ML:

• Google: “Machine Learning books”

• Google: “Machine Learning using R book”

• Google: “Machine Learning with R book” etc.

Must Visit Online Books Web Site

Bookdown

More Books

Machine Learning Yearning, Andrew Ng

Pattern Recognition and Machine Learning, Bishop, Christopher

Applied Predictive Modeling By Max Kuhn and Kjell Johnson

Hands-On Machine Learning with R

…

Tutorials, Videos, Lectures

Lots of.. Here is a very limited list you can access over internet.

Over 200 of the Best Machine Learning, NLP, and Python Tutorials, 2018 Edition

MIT OpenCourseWare, Prediction: Machine Learning and Statistics

…

An Extra for R:

UCLA Institute for Digital Research and Education

ML with R

Machine Learning in R

An Introduction to Machine Learning with R

Course Content: Just Keywords

• Data - Model and Analysis

  • Data Types
    
  • Where to Start: Exploratory Data Analysis (A Short Discussion)
    
  • Summaries
    
  • Visualization

• Causality: Short Discussion

• Regression: An Overview

  • Regression With Real Values
    
  • Regression With Nominal/Ordinal Values

• Regularization

  • Ridge Regression
    
  • Least Absolute Shrinkage and Selection Operator (LASSO) Regression
    
  • Elastic Net

• More Regressions

  • Polynomial Regression
    
  • Spline Regression
    
  • Generalized Additive Models

• Bagging And Boosting: An Overview

  • Random Forests
    
  • Adaptive Boosting (Adaboost)
    
  • Gradient Boosting and Extreme Gradient Boosting XGBoost

• Similarity Measures

• Clustering

  • Agglomerative Hierarchical Clustering, (AHC)
    
  • K-means Clustering
    
  • Cluster Validity Measures
    
  • Soft-Clustering (If time permits)

• Neural Networks

Course Content: Topics Discussed

• Causality

• Time Series/Cross-Sectional Data

• Learning: Supervised, Unsupervised (and ?Reinforcement)

• Bootstrapping

• Bagging and Boosting

• Similarity

• Sampling

• Black-Box Models

Course Content: Steps of ML

• Data Collection/preparation

• Meet the Data

• Preprocessing

• Knowledge Extraction

  • Feature Selection (Important Explanatory Variables)
    
  • Feature Engineering
    
  • Model Selection/Model Committee
    
  • Model Diagnostics

• Testing and Validation

SW Resources, R

R-Internet Sitesi

RStudio

RStudio

Rmarkdown (rmd): Reproducible Reports

RStudio

Grading

• Based on:
  • Attendance: \(weight \leq 10%\)
  • HWs and Contribution of in-class discussion: \(weight \leq 40%\)
  • Term Project Presentation: \(weight \geq 30%\)
  • Final Exam: \(weight \geq 50%\)