Description
In the age of Artificial Intelligence, 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 machine learning 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.
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.
Course prerequisites
Participants are expected to be familiar with basic statistical modeling (as for instance taught in Module 4 of this program), and to have had a first experience programming in Python (as for instance taught in Module 5 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 2025: April 28 and May 5, 12, 19 & 26 and June 2 and 16, 2025, from 5.30 pm to 9 pm.
Venue
Faculty of Science, Campus Sterre, Krijgslaan 281, 9000 Ghent
Course material
Access to the slides and Python code notebooks
Fees
The participation fee is 1470 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations. An exam fee of 35 EUR will be applied.
- Industry, private sector, profession*: € 1470
- Non profit, government, higher education staff: € 1105
- (Doctoral) students, unemployed: € 600 | Full reimbursement by Doctoral Schools is possible (see details below).
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
Registration
To register, add the course below to your shopping cart and proceed to checkout.
Is this your first registration for a Beta Academy course? In that case, you will need to create an account first. Afterward, you will receive a confirmation email to activate your account on the academy platform. You do not have to click on the activation link but can immediately return to your shopping cart to complete your course registration. If you do not receive a confirmation email for your course order, please contact our Science Academy at science-academy@ugent.be.
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UGent PhD students
Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.
By registering for a course or event organized by the Science Academy, you agree to the cancellation procedure that you can find on our website
KMO-portefeuille
Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Organisation
Science Academy (IPVW)
Faculty of Science
science-academy@ugent.be
Website
