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”). This data is collected on people, companies and institutions, web and mobile devices and satellites, at an 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. It's primary objective is that of turning information into knowledge and value by “letting the data speak”. Machine learning limits prior assumptions about data structure, and relies on a model-free philosophy that supports algorithm development, computational procedures, and graphical inspection more than tight assumptions, algebraic development, and analytical solutions. Machine learning was computationally unfeasible up until a few years ago. It is only possible on the machines of today, with their increased computing power and ability to learn, their hardware development, and with continuous software upgrading.
This course is a primer to machine learning techniques using Stata. Stata owns 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 Stata's 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 evenly balance theory and practical sessions.
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, 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
No prior knowledge of machine learning techniques are required to attend this course, as the first session will start from scratch with a fresh introduction to the subject. 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 mainly based on the graphical language and intuition more so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.
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:
- (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.