Machine learning and deep learning fundamentals

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, you will learn how to process data, build regression and classification models, and train neural networks and convolutional neural networks. 

By the end of the training, you will understand the fundamentals of machine learning and deep learning and gain practical skills to start applying these techniques to your research projects.

Learning outcomes

• 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