Online, Italy

Introduction to Machine Learning

Recent years have witnessed an unprecedented increase in the availability of information on social, economic and health-related phenomena. Today researchers, professionals and policy makers have access to enormous databases, containing detailed information on individuals, companies and institutions and use of mobile devices. Machine Learning, a relatively new approach to data analytics, which lies at the intersection between statistics, computer science and artificial intelligence, has involved rapidly over recent years in response to our need to analysis these so-called Big Data sets. In contrast to the more tradition approach of data analysis, which focuses on prior assumptions relating to data structure and the derivation of analytical solutions, Machine Learning techniques rely instead on a model-free philosophy development of algorithms, computational procedures, and graphical inspection of the data in order to more accurately predict outcomes. The underlying approach taken is then to “let the data speak for itself”. Computationally infeasible until very recently, Machine Learning is itself a product of the latest advancements in both Information Technology and computing power.

This introductory course offers an intensive overview of the standard Machine Learning algorithms currently applied to social, economic and public health data, using a series of both official and community written Stata, Python and R commands. The primary objective being to illustrate how Machine Learning techniques can be applied to search for patterns in large databases, which can subsequently be used by researchers, professionals and policy makers alike to make both decisions and predictions. As a by-product, the course also serves to increase awareness as to Python and Stata’s “joint” capabilities to derive knowledge and value from large and often ‘noisy’ databases, through the use of both official and user written Machine Learning routines developed, which to date still remain relatively unknown to the majority of users.

COURSE STRUCTURE
The online format of our introductory Machine Learning course has been divided into two distinct modules, allowing researchers already familiar with the arguments discussed in the first module to choose to participant only in the second module.

The first module offers participants an overview of Python and Stata’s Machine Learning capabilities for data management, data quality analysis; exploratory data analysis, feature engineering and Principal Component Analysis. The second module instead focuses of the following popular Machine Learning methodologies: Supervised Learning, data management for Supervised Learning, Predictive Models, Logistic Regression, Stepwise Regression, Decision Trees, Neural Networks and Hyperparameter Optimization and Model Validation.

In common with TStat’s training philosophy, each individual session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an extensive applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced by case study examples, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, the course leader is able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data.

COURSE OUTCOMES

At the end of the course, it is expected that participants:

have an understanding of the fundamental concepts and principles of Machine Learning;
are able to use Stata, R, and Python for data exploration, visualization, and preprocessing data in a Machine Learning context;
can independently implement the popular Machine Learning algorithms using Stata, R, and Python;
have attained an understanding of “real world” data issues through this hands-on experience; and
are able to implement solutions (with the help of the Stata and Python routine templates specifically developed for the course) to real world issues using Machine Learning techniques.

Target group

This course has been specifically developed for both professionals, researchers and Ph.D students working in advertising, business and management, biostatistics, economics, marketing, public health and social sciences, interested in applying the latest Machine Learning techniques in Stata and Python to big data.

Course aim

This introductory course offers an intensive overview of the standard Machine Learning algorithms currently applied to social, economic and public health data, using a series of both official and community written Stata, Python and R commands. The primary objective being to illustrate how Machine Learning techniques can be applied to search for patterns in large (often extremely “noisy”) databases, which can subsequently be used by researchers, professionals and policy makers alike to make both decisions and predictions. As a by-product, the course also serves to increase awareness as to Python and Stata’s “joint” capabilities to derive knowledge and value from large and often ‘noisy’ databases, given the use of packages (both official and user written) for performing Machine Learning which still remain relatively unknown to the majority of their users.

Fee info

EUR 520: MODULE 1: CODE D-EF46-OL (2 online sessions)
Full-Time Students*: € 520.00
Ph.D. Students: € 670.00
Academic: € 765.00
Commercial: € 1025.00

MODULE 2: CODE D-EF47-OL (2 online sessions)
Full-Time Students*: € 520.00
Ph.D. Students: € 670.00
Academic: € 765.00
Commercial: € 1025.00