Online Machine Learning with Stata Masterclass

Online Machine Learning with Stata Masterclass

Master both the fundamentals and advanced techniques of machine learning using Stata in this flexible 4-day course. Students can choose to attend the full course or select either the Introductory (5–6 June) or Advanced (12–13 June) sessions individually. With hands-on training and expert instruction, you'll gain practical skills to extract insights from complex data using Stata's powerful machine-learning tools.

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US$ 360,00
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2 - 4 Days (Flexible)
Online
Stata

Course Overview

 

In a data-rich world, the ability to transform complex, high-volume datasets into meaningful insights is a vital skill across scientific, policy, and business domains. Machine Learning with Stata is a comprehensive 4-day training course that introduces and deepens your understanding of machine learning techniques using Stata — with a flexible format that allows attendance of either the full course or just the introductory or advanced 2-day components.

 

Designed with a balance of theory and hands-on practice, this course leverages Stata’s powerful machine learning packages to cover both foundational and advanced techniques. Whether you're just beginning or looking to expand your ML toolkit, this course will help you harness Stata for predictive modeling, classification, variable selection, and more — all through intuitive, graphical learning approaches rather than abstract algebra.

 

The course is suitable for researchers, analysts, and professionals across disciplines, especially those working with social, economic, and health data.

 

Course Structure and Highlights

 

Day 1–2: Introduction to Machine Learning with Stata (5 & 6 June 2025)

Ideal for beginners or those looking to refresh foundational concepts.

  • Introduction to Machine Learning: concepts, goals, and key distinctions (e.g. supervised vs. unsupervised learning)

  • Inference vs. prediction, sampling vs. specification error

  • Goodness-of-fit, validation techniques, bias-variance trade-off

  • Model selection and regularization:

  • Hands-on Stata sessions with real-world examples

 

Day 3–4: Advanced Machine Learning with Stata (12 & 13 June 2025)

For participants ready to explore more powerful and nuanced methods.

  • Classification techniques:

  • Neural networks:

  • Ensemble methods and non-linear modeling:

  • Kernel-based and global regression methods:

  • Polynomial, spline, and series regressions

  • Practical applications in Stata with an emphasis on interpretability and prediction power

 

What You Will Learn

 

By the end of the course, participants will be able to:

  • Understand and apply a broad range of machine learning methods in Stata
  • Select and tune models using cross-validation and regularisation techniques
  • Classify and predict outcomes using supervised learning tools
  • Extract signal from noise and detect variable importance
  • Apply machine learning in real-world research scenarios, from causal inference to data mining

 

Who Should Attend?

 

This course is open to participants from all scientific disciplines, though it is especially designed for:

  • Researchers in medical, epidemiological, and socio-economic fields

  • Data analysts and policy professionals

  • Students and academics looking to integrate machine learning into their research

 

No prior knowledge of machine learning is required for the introductory sessions. The advanced sessions assume a basic familiarity with regression and classification concepts.

 

Format & Delivery

  • Duration: 4 days (can be attended as two separate 2-day modules)

  • Mode: Live, instructor-led with practical Stata exercises

  • Materials Provided: Lecture slides, datasets, Stata code templates, and reference guides

Agenda

An Introduction to Machine Learning
Day One: 5 June 2025

The Basics of Machine Learning

Machine Learning: definition, rational, usefulness

  • Supervised vs. unsupervised learning
  • Regression vs. classification problems
  • Inference vs. prediction 
  • Sampling vs. specification error

 

Coping with the fundamental non-identifiability of E(y|x) 

  • Parametric vs. non-parametric models
  • The trade-off between prediction accuracy and model interpretability

 

Goodness-of-fit measures

  • Measuring the quality of fit: in-sample vs. out-of-sample prediction power
  • The bias-variance trade-off and the Mean Square Error (MSE) minimization
  • Training vs. test mean square error
  • The information criteria approach

 

Estimating training and test error

  • Validation set, K-fold cross-validation, and the Bootstrap
Model Selection as a Correct Specification Procedure
  • Model selection as a correct specification procedure
  • The information criteria approach

 

Subset Selection

  • Best subset selection 
  • Backward stepwise selection 
  • Forward stepwise Selection

 

Shrinkage Methods 

  • Lasso and Ridge, and Elastic regression
  • Adaptive Lasso
  • Information criteria and cross validation for Lasso

 

Stata implementation

An Introduction to Machine Learning
Day Two: 6 June 2025

Discriminant Analysis and Nearest-neighbor Classification
  • The classification setting
  • Bayes optimal classifier and decision boundary
  • Misclassification error rate

 

Discriminant analysis

  • Linear and quadratic discriminant analysis 
  • Naive Bayes classifier

 

  • The K-nearest neighbors classifier
  • Stata implementation
Neural Networks

  • The neural network model

      • neurons, hidden layers, and multi-outcomes 

     

    Training a neural networks 

      • Back-propagation via gradient descent 
      • Fitting with high dimensional data
      • Fitting remarks

     

      • Cross-validating neural network hyperparameters
Final Session: 1 hour Q&A with the instructor
Advanced Machine Learning
Day One: 12 June 2025

Recap of the Basics of Machine Learning
  • Machine Learning: definition, rational, usefulness
  • Coping with the fundamental non-identifiability of E(y|x)
  • Goodness-of-fit measures
  • Estimating training and test error
  • Tuning hyper-parameters optimally
Nonparametric Regression

Beyond parametric models: an overview

Local, semi-global, and global approaches:

  • Kernel-based and nearest-neighbor regression
  • Polynomial and series estimators
  • Piecewise polynomials and spline regression
  • Generalised additive models
  • Partially linear models

 

Stata Implementation

Advanced Machine Learning
Day Two: 13 June 2025

Tree Based Methods

Regression and Classification trees: an introduction

  • Growing a tree via recursive binary splitting
  • Optimal tree pruning via cross-validation

 

Tree based ensemble methods

  • Bagging
  • Random forests
  • Boosting

 

Stata implementation

Practicing Machine Learning with Stata
  • Stata commands for supervised Machine Learning: an overview
  • The Stata commands r_ml_stata_cv and c_ml_stata_cv
  • Application to real datasets
Final Session: 1 hour Q&A with the instructor

Prerequisites

Knowledge of basic statistics, Stata and econometrics is required, including:

  • The notion of conditional expectation and related properties;
  • point and interval estimation;
  • regression model and related properties;
  • probit and logit regression.

 

Reading List:

  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, T., Tibshirani, R., Friedman, J., Springer (2009)
  • An Introduction to Statistical Learning, Gareth, J., Witten, D., Hastie, T., Tibshirani, R., Springer (2013)
  • Microeconometrics Using Stata, Cameron e Trivedi, Revised Edition, StataPress (2010)
  • A Super-Learning Machine for Predicting Economic Outcomes, Giovanni Cerulli

Course Timetable

Subject to minor changes

Morning Session

Afternoon Session

Q&A with Instructor

10am-12pm (London time)

2pm-4pm (London time)

4pm-4:30pm (London time)

10am-12pm (London time)

2pm-4pm (London time)

4pm-4:30pm (London time)

 

Terms

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Temporary, time limited licences for the software(s)  used in the course will be provided. You are required to install the software provided prior to the start of the course.
  • Payment of course fees required prior to the course start date.
  • Registration closes 1-calendar day prior to the start of the course.

 

  • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
  • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
  • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

 

Delivered By

Student Testimonials

Giovanni's delivery is fantastic; makes great connections between new and prior knowledge and focuses on the key strengths and limitations of the discussed methods. Excellent course design that builds on the Introductory Machine Learning course and knowledge acquired in the PhD Econometrics sequences of courses. This is all nicely supplemented by detailed Stata code with explanations and sample datasets. 

Excellent course and great explanations on ML techniques and applications from Giovanni ! I leanred so much including the coding and applications plus the fundamentals of ML.

The 'Advanced Machine Learning (AML)' experience was excellent for trying to gain more experience in Statistics using links Python and STATA.  

I'm not a Statistician! However, Giovanni managed to link the 'Fundamentals of Machine Learning (FML) ' to 'Advanced Machine Learning' in his usual excellent way. When starting the AML, for me I am pleased that the FML was a tremendous help and allowed me to use my mathematical knowledge for Physics and Science. I'm looking forward to Giovanni's next course (using large datasets) and his book.

Linking my knowledge of mathematics (from Science and Engineering) to Statistics. I do hope it is leading towards becoming better at 'Medical Statistics' that require very large datasets...and a big thank you to Giovanni!

Very well organized, very useful and relevant content, looking forward to joining future events!

As always great service and real good courses. In addition, thanks to Professor Cerulli for making himself understood in the best way.

The delivery of this course was exceptionally well done. It really helped me to appreciate the concepts as well as the practical applications in Stata. If you are new to this topic, this will provide a good introduction to complex issues.

Very easy to communicate, all emails contained all the information necessary. I think that the course was very well structured and organized. The tutor provided a number of codes that were extremely helpful for understanding. Overall, very useful and easy to follow!

I highly appreciated Professor Giovannu Cerulli course. The classes notes are very clear   and well prepared with an extensive coverage of the course subjects. And they are simultanesouly quite objective by focusing on the most important contents. Professor Giovannu Cerulli lectures are very didatic which greately helps the easily assimilation of the   corespondent knowledge. Furthermore, the course materials are quite   comprehensive and they englobe not only the classes notes, but also the referenced papers as well as data and Stata programs to estimate the models in this software. All in all, I greatly recommend this   course, as it really amazingly speeds up the acquaintance of the underlying theory and appied aplication in a very short period of time.

I found the Stata Summer School 2021 very useful and interesting. The course was perfectly structured and organised, with a good progression during the week. The instructors presented the topics covered in an easy and understandable way. There were room for questions and answers when needed. Materials shared for the course were tidy and informative, and I am sure I will use them frequently. This course was arranged online, which in my opinion worked very well. I believe the course delivered as promised and according to information found online when I signed up for the course. Easy to purchase/sign up for the course. User friendly. Quick and timely response.

Very efficient in terms of communication and delivery. Provides a very comprehesnive applied knowledge of stata. I would definitely recommend others to buy from them.

I went UK University of Cambridge for a summer school with Timberlake, it was excellent.

It was a great course and I thoroughly enjoyed it. Many of my fellow participants were eager to share their ideas. I thought the course could help further many people in a similar stage to my career!