Introduction to Supervised Learning with Python
Supervised machine learning plays a pivotal role in the broader field of artificial intelligence (AI) and is crucial for driving new developments and innovations in various domains. This branch of data science entails many interesting applications to get insights into your data and make predictions.
Practical information:
Want to register?
- Prerequisites: basic knowledge of the Python programming language
- Price: determined upon registration
Georganiseerd door:
The course is designed as an introductory course to get a foundational understanding of some basic supervised machine learning techniques. These techniques are accompanied by Python code demonstrations and by means of the scikit-learn package to try some of these data pipelines yourself. The goal of the course is to equip students with the essential knowledge and skills required to dive deeper into the world of more advanced machine learning techniques.
The following topics will be covered:
- Linear models
- Cross-Validation
- Regularization
- KNN models
- Tree-Based Models
- SVM models
- Interpretability
Prerequisites
Participants need to have a basic knowledge of the Python programming language, in particular importing and wrangling DataFrames with the pandas package and visualizing data with the matplotlib/seaborn package.
Teacher / speaker
Axel-Jan Rousseau
Dr. Axel-Jan Rousseau is an AI Expert for VAIA and a Postdoctoral Researcher at Hasselt University. Specializing in trustworthy computer vision, his work focuses on model calibration, ensuring AI systems provide reliable, statistically sound predictions.
He is also one of the coordinators for the Data Science track for FLAMES and develops AI-driven decision support systems for biomedical imaging and MS diagnosis. He holds a PhD in AI (2024) and Master's degrees in both AI and Industrial Engineering.
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