Machine Learning with Python-M11

2025M11

Engels

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.

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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.

We follow the No Show polity of the Doctoral School!