Machine Learning Using Stata Course

Course Level: Foundation

Course Objectives

At the end of this IRES training course, participants will learn:

1. Understanding the fundamentals: Gain a solid understanding of the basic concepts and principles of machine learning, including supervised and unsupervised learning, model training, evaluation, and interpretation.
2. Familiarity with Stata's machine learning capabilities: Learn how to utilize Stata's built-in machine learning tools and functions, such as regression models, decision trees, support vector machines, neural networks, clustering algorithms, and dimensionality reduction techniques.
3. Data preparation and exploration: Learn how to clean and preprocess data in Stata, handle missing values, perform exploratory data analysis, and engineer relevant features for machine learning tasks.
4. Model selection and evaluation: Develop the ability to choose appropriate machine learning algorithms for different types of problems, understand their strengths and limitations, and evaluate model performance using various metrics and cross-validation techniques.
5. Interpretation and validation: Learn how to interpret model coefficients, feature importance, and other relevant outputs in Stata. Understand how to validate and assess model performance using external datasets and appropriate validation techniques.
6. Practical application and deployment: Gain hands-on experience in applying machine learning techniques to real-world problems using Stata. Learn how to deploy trained models, generate predictions, and incorporate machine learning into practical workflows.
7. Advanced topics and techniques: Explore advanced topics in machine learning with Stata, such as handling imbalanced datasets, ensemble methods, time series analysis, and forecasting. Gain knowledge of best practices, ethical considerations, and addressing biases in machine learning projects.

Course Outline

Module 1: Introduction To Machine Learning

• Understanding the basics of machine learning
• Differentiating between supervised and unsupervised learning
• Overview of common machine learning algorithms and their applications

Module 2: Data Preparation And Exploration

• Data cleaning and handling missing values in Stata
• Exploratory data analysis techniques
• Feature selection and engineering in Stata

Module 3: Supervised Learning Algorithms In Stata

• Linear regression and logistic regression
• Decision trees and random forests
• Support vector machines (SVM)
• Neural networks and deep learning

Module 4: Model Training And Evaluation

• Training machine learning models in Stata
• Cross-validation techniques
• Evaluating model performance using metrics like accuracy, precision, recall, and AUC-ROC

Module 5: Unsupervised Learning Algorithms In Stata

• Clustering algorithms (e.g., k-means, hierarchical clustering)
• Dimensionality reduction techniques (e.g., principal component analysis, t-SNE)

Module 6: Model Interpretation And Validation

• Interpreting model coefficients and feature importance in Stata
• Validating model performance using external datasets and bootstrapping techniques

Module 7: Advanced Topics In Machine Learning With Stata

• Handling imbalanced datasets
• Ensemble methods (e.g., bagging, boosting)
• Time series analysis and forecasting with machine learning in Stata.