Course Overview: Part one
Recent years have witnessed an unprecedented availability of information on social, economic, and health-related phenomena. Researchers, practitioners, and policymakers have nowadays access to huge datasets (the 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 that of turning information into knowledge and value by “letting the data speak”. To this purpose, 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. Stata owns today various packages to perform machine learning which are however poorly known to many Stata users. This course fills this gap by making participants familiar with (and knowledgeable of) Stata potential to draw knowledge and value form row, large, and possibly noisy data. The teaching approach will be mainly based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.
After the course, participants are expected to have an improved understanding of Stata potential to perform some of the most used marching learning techniques, thus becoming able to master research tasks including, among others:
- (i) factor-importance detection
- (ii) signal-from-noise extraction
- (iii) correct model specification
- (iv) model-free classification, both from a data-mining and a causal perspective.
Course Overview: part 2
This course will focus on three specific techniques not covered in the first-part 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 mainly based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.
After the course, participants are expected to have an improved understanding of Stata potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including, among others:
- (i) factor-importance detection,
- (ii) signal-from-noise extraction,
- (iii) model-free regression and classification, both from a data-mining and a causal perspective.
The course is open to people coming from all scientific fields ,but it is particularly targeted to researchers working in the medical, epidemiological and socio-economic sciences.