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. Through a mix of clear explanations and hands-on exercises in Jupyter notebooks, participants will learn how to process data, build regression and classification models, and train neural networks and convolutional neural networks.

Objectives

• 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 the 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 

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Organized by VIB Training and Conferences and ELIXIR Belgium