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 few years ago, machine learning is a product of the computer’s era, of today machines’ computing power and ability to learn, of hardware development and continuous software upgrading.
This course is a primer to machine learning techniques using Stata. Today, various machine learning packages are available within Stata, but some of tghese 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 the 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.
After the course, participants are expected to have an improved understanding of Stata's potential to perform machine learning, becoming able to master research tasks including, among others:
- factor-importance detection,
- signal-from-noise extraction,
- correct model specification,
- model-free classification, both from a data-mining and a causal perspective.
Session 1: 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
- Measuring the quality of fit: in-sample vs. out-of-sample prediction power
- Goodness-of-fit indices
- The bias-variance trade-off and the Mean Square Error (MSE) minimization
Session 2: Simulation, Resampling, and Validation Methods
Monte Carlo simulations
- Logic and functioning of a Monte Carlo experiment
- Implementing Monte Carlo experiments via
- The logic of the Bootstrap
- Bootstrapping standard errors via
- The validation set approach
- Leave-One-Out Cross-Validation
- K-fold cross-validation
- The Stata package
Session 3: Non-parametric Regression - Local Methods
- Beyond parametric models: the “why” and the “how”
- Type of non-parametric regressions: local vs global approaches
- Nearest-neighbor regression
- Kernel-based regression
- The Stata
Session 4: Non-parametric Regression - Global Methods
Monte Carlo simulations
- Polynomial and series regression with
- Spline regression with
- Generalized additive models with
Session 5: Model Selection and Regularization
- Optimal subset selection with
- Lasso, Ridge, and Elastic regression with
- Model uncertainty and credibility
- The LOCO sensitivity algorithm via
Session 6: Tree-based Regression
- An introduction to Regression Trees
- Bagging, Random Forests, and Boosting
- The R-based Stata command
Pre-course Reading List
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2013), An Introduction to Statistical Learning with Applications in R, Springer, New York, 2013. ISBN # 978-1-4614-7137-0. See Amazon for hardcover or eTextbook.
Post-course Reading List
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2008), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition, Springer
|Time||Session / Description|
||Arrival & Registration
||Tea/coffee break (Feedback Session)
Some 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.
Terms & Conditions
- 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. Contact us for more information.
- Cost includes all course materials, lunch and refreshments.
- 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. Alternatively, laptops can be hired for a fee of $20.00 per day.
- If you need assistance in locating hotel accommodation in the region, please notify us at the time of registration.
- Full payment of course fees is required prior to the course start date to guarantee your place.
- Registration closes 5-calendar days prior to the start of the course.
Cancellations or changes to your registration
- 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.
The number of seats available is restricted. Please register early to guarantee your place.