The rise of machine learning across all sectors has led to increased use of predictive models<\/strong> for a wide range of complex, high-stakes issues\u2014especially those involving risks such as false positives, algorithmic bias, and model drift.<\/p>\n Many organizations now seek engineers who not only develop, validate, and deploy predictive algorithms but also understand their limitations and apply rigorous statistical methods to inspect them. In critical fields, where decisions have significant consequences, models must be thoroughly analyzed, strictly controlled, and inspire confidence<\/strong>.<\/p>\n This specialization deepens expertise in machine learning, focusing on current high-impact challenges in artificial intelligence: image processing, model explainability and interpretability, and trustworthy AI<\/strong>. The curriculum includes new subjects, particularly in optimal transport, model explainability, and prediction uncertainty quantification (conformal prediction).<\/p>\n