Target audience

This course targets professionals and investigators from all areas that are involved in predictive modeling based on large and/or high-dimensional databases.

Description

Many modern digital applications increasingly rely on machine learning as a means to derive predictive strength from high-dimensional data sets. Compared to traditional statistics, the absence of a focus on scientific hypotheses, and the need for easily leveraging detailed signals in the data require a different set of models, tools, and analytical reflexes.

This course aims to bring participants to the level where they can independently tackle the analytical part of data mining projects. This means that the most common types of projects will be addressed - regression-type with continuous outcomes, classification with categorical outcomes, and clustering. For each of these, the practical use of a set of standard methods will be shown, like Random Forests, Gradient Boosting Machines, Support Vector Machines, k-Nearest-Neighbors, K-means,... Furthermore, throughout the course, concepts will be highlighted that are of concern in every statistical learning applications, like the curse of dimensionality, model capacity, overfitting and regularization, and practical strategies will be offered to deal with them, introducing techniques such as the Lasso and ridge regression, cross-validation, bagging and boosting. Instructions will also be given on a selection of specific techniques that are often of interest, such as modern visualization of high-dimensional data, model calibration, outlier detection using isolation forests, explanation of black-box models,... Finally, the last lecture will introduce the idea of deep learning as a powerful tool for data analysis, discussing when and how to practically use it, and when to shy away from it.

Course prerequisites

Participants are expected to be familiar with basic statistical modeling (as for instance taught in Module 3 of this program), and to have had a first experience programming in Python (as for instance taught in Module 4 of this program).

Exam / Certificate

If you take part in all 7 sessions you will receive a certificate of attendance via e-mail after the course ends.

Additionally, you can take part in an exam. If you succeed in this test a certificate from Ghent University is issued.
The exam consists of a take home project assignment. You are required to write a report by a set deadline.

Type of course

This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a class on campus.

Schedule

Seven Monday evenings in April, May and June 2023: April 17 and 24, May 8, 15 and 22, June 5 and 12, 2023, from 5.30 pm to 9 pm.

Venue

UGent, Faculty of Science, Campus Sterre, Krijgslaan 281, Ghent. Building S9, 3rd floor, pc room 3.1 (Konrad Zuse).

Teacher

As the head of Advanced Analytics and Machine Learning at KPMG, dr. Bart Van Rompaye heads a group of data scientists applying modern data analytical approaches to a broad range of problems in a wide variety of sectors. Before this, Bart was active for 6 years as a Lead Data Scientist within KBC Group, creating products such as Matti, Indigo (Czech Republic) and the first AI-assisted investment fund in Belgium. He obtained his PhD at Ghent University on issues in survival analysis, and held postdoctoral positions at Ghent University and Umea University, Sweden. In the past, he has taught numerous courses for the Master in Statistical Data Analysis, the Institute for Continuing Education in Science, and FLAMES, the Flanders Training Network for Methodology and Statistics.

Course material

Access to the slides and Python code notebooks

Fees

A different price applies, depending on your main type of employment.

*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment.

Register

Register for this course

UGent PhD students

As UGent PhD student you can incorporate this 'specialist course' in your Doctoral Training Program (DTP). To get a refund of the registration fee from your Doctoral School (DS) please follow these strict rules and take the necessary action in time. The deadline to open a dossier on the DS website (Application for Registration) for this course is March 17, 2023.

Opening a dossier with your DS does not mean that you are enrolled for the course with our academy. You still need to enrol via the registration form on this site.

It is you or your department that pays the fee first to our academy. The Doctoral School refunds that fee to you or your department once the course has ended.

It is not obligatory to participate or succeed in the exam.