Machine learning and deep learning fundamentals
Machine learning and deep learning fundamentals
1 April 2026, 9h00-17h00
2 April 2026, 9h00-17h00
General context
Machine learning and deep learning are transforming research by enabling data-driven discoveries and predictive modeling. For many researchers, these techniques can unlock new insights from complex datasets, but getting started can feel overwhelming. This two-day workshop is designed for researchers with little or no prior experience in machine learning who want to apply these methods in their own work.
- Explain the fundamental concepts of machine learning and deep learning
- Apply data preprocessing techniques such as handling missing values, scaling features, and splitting datasets
- Implement regression and classification models with scikit-learn and evaluate their performance using appropriate metrics
- Construct simple neural networks and convolutional neural networks with PyTorch framework
- Identify issues with model behavior, like overfitting, and adopt solutions such as regularization or dropout
- Interpret the results of machine learning and deep learning models to make informed decisions for research applications
Through a mix of clear explanations and hands-on exercises in Jupyter notebooks, you will learn how to process data, build regression and classification models, and train neural networks and convolutional neural networks.
- No prior machine learning experience
- Familiarity with Python will be required (recommended course: Programming in Python: master your first project)
- Jupyter Notebook
- Scikit-learn
- PyTorch
This course is part of the learning path Machine learning.
AI methods used
The course covers supervised machine learning methods (regression and classification) using scikit-learn, and deep learning methods (neural networks and convolutional neural networks) using PyTorch. Participants will apply these methods, focusing on how each technique supports predictive modeling.
Trainers
Jolan Heyse
Jolan Heyse is a trainer at VIB specializing in artificial intelligence and data science for life sciences. He holds a master's degree and a PhD in Biomedical Engineering from Ghent University, where his research focused on applying AI to EEG-based epilepsy diagnosis. Before joining VIB, Jolan worked as a data scientist at AZ Delta, developing AI applications for healthcare. His expertise includes machine learning, biomedical signal processing, Large Language Models (LLMs), and translating complex algorithms into practical tools for researchers. At VIB, he designs and delivers training programs to help scientists with data-driven methods.
Program
Welcome and set-up
Introduction to machine learning
Coffee break (provided)
Regression
Lunch (provided)
Classification
Coffee break (provided)
Model selection
Introduction to neural networks
Coffee break (provided)
Deep neural networks
Lunch (provided)
Convolutional neural networks
Coffee break (provided)
Advanced deep learning topics
Wrap-up
Practical info
01 April 2026 - 02 April 2026
Ghent - VIB/UGent FSVM II
Technologiepark 75
9052 Zwijnaarde
Belgium
01 April 2026 - 02 April 2026
Ghent - VIB/UGent FSVM II
From Ghent Sint-Pieters station, you can take a bus to Technologiepark. Please check Routeplanner De Lijn for schedules.
01 April 2026 - 02 April 2026
Ghent - VIB/UGent FSVM II
Looking for a custom cycling route? Try the online cycling route planner to easily and quickly identify the fastest, safest and/or most scenic route to any destination in Ghent.
Cycling route planner
Shared bicycles:
Several providers are active in the area.
Take a look at their platforms:
- Indigo Weel Pro app
- Bluebike
- DOTT
- BOLT eBikes
- Donkey Republic
There are also some public bicycle pumps at the park.
01 April 2026 - 02 April 2026
Ghent - VIB/UGent FSVM II
There is only one entrance to Technologiepark. At the entrance, please take a ticket - Parking is only allowed in regular parking spots (for instance in front of the building) and in the parking tower. Parking alongside the roads or in other places where there is no regular parking is prohibited.