Advanced Machine Learning with Stata

Recent years have witnessed an unprecedented availability of information on social, economic, and health-related phenomena. Researchers, practitioners, and policymakers now have access to huge datasets (so-called “Big Data”) on people, companies and institutions, web and mobile devices, satellites, etc., at increasing speed and detail.

Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. Its primary objective is to turn information into knowledge and value by “letting the data speak”. Machine learning limits prior assumptions on data structure, and relies on a model-free philosophy supporting algorithm development, computational procedures, and graphical inspection more than tight assumptions, algebraic development and analytical solutions. Computationally unfeasible a few years ago, machine learning is a product of the computer’s era, of today machines’.

Today, various machine learning packages are available within Stata, but some of these are not known to all Stata users. This course fills this gap by making participants familiar with Stata's potential to draw knowledge and value from rows of large and possibly noisy data. The teaching approach will be based on graphical language and intuition more than on algebra. The sessions will make use of instructional as well as real-world examples and will balance theory and practical sessions evenly.

This training course is part two of our Machine Learning in Stata series; this course will build on methods taught in our Introduction to Machine Learning using Stata training.

After the course, participants are expected to have an improved understanding of Stata's potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including:

• Factor-importance detection,
• Signal-from-noise extraction,
• Model-free regression and classification, both from a data-mining and a causal perspective.

Meet Dr Giovanni Cerulli, giving an overview of the course.

Some prior knowledge of machine learning techniques is required to attend this course, however, the first session will start from scratch with a fresh introduction to the subject to refresh your knowledge. This course will focus on three specific techniques not covered in the first part of the course, that is: regression and classification trees (including bagging, random forests, and boosting), kernel-based regression, and global methods (step-wise, polynomial, spline, and series regressions).

The teaching approach will be based mainly on graphical language and intuition more than on algebra. The training will use instructional and real-world examples and will evenly balance theory and practical sessions.

Watch Dr Giovanni Cerulli's expertly instructed Machine Learning Regression guide now. In this video demonstration, Giovanni uses the command r_ml_Stata. Some of the model types you are able to create from this command include Elastic net, Regression tree, Neural network, Boosting, Support Vector Machine and Bagging and random forests.

• Informed Decision-Making in Various Domains: Participants will be empowered to apply machine learning techniques in diverse fields, such as social sciences, economics, and health. This knowledge will enable them to make informed decisions based on insights extracted from large datasets.
• Enhanced Research Capabilities: Researchers can apply the learned techniques to enhance their research methodologies. The course's focus on correct model specification and model-free classification ensures robust analysis, contributing to the reliability of research findings.
• Efficient Data Utilization: Professionals and policymakers will benefit from the ability to extract valuable information from large and possibly noisy datasets. This efficiency in data utilization can lead to improved policy formulation, strategic planning, and business decision-making.

The course is open to people from all scientific fields, but it is mainly targeted at researchers working in the medical, epidemiological and socio-economic sciences.

Final Session: 1 hour Q&A with the instructor

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

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 that 14-calendar days prior to the start of the course