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.
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 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:
correct model specification,
model-free classification, both from a data-mining and a causal perspective.
This course is a primer to any more advanced machine learning courses within Stata.
Watch the video below for a preview from our course leader, Dr Giovanni Cerulli.
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
The bias-variance trade-off and the Mean Square Error (MSE) minimization
Training vs. test mean square error
The information criteria approach
Machine Learning and Artificial Intelligence
2. Model selection as a correct specification procedure
The information criteria approach
Best subset selection
Backward stepwise selection
Forward stepwise Selection
Lasso and Ridge, and Elastic regression
Information criteria and cross validation for Lasso
DAY 2: 3. Discriminant analysis and nearest-neighbor classification
The classification setting
Bayes optimal classifier and decision boundary
Misclassification error rate
Linear and quadratic discriminant analysis
Naive Bayes classifier
The K-nearest neighbours classifier
4. 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
Cross-validating neural network hyperparameters
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. 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.
It is required some knowledge of basic statistics and econometrics: notion of conditional expectation and related properties; point and interval estimation; regression model and related properties; probit and logit regression.
Basic knowledge of the Stata software
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.
Delegates are provided with temporary licences for the principal software package(s) used in the delivery of the course. It is essential that these temporary training licenses are installed on your computers 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 more than 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 attendees is restricted. Please register early to guarantee your place.