The aim of this course is to introduce the theoretical foundations of data analytics and machine learning, enable the analysis of large and complex datasets, and evaluate the interpretability of predictive models. Students will gain practical knowledge of data preprocessing, dimensionality reduction, predictive modeling, ensemble learning techniques, and explainable machine learning approaches.

This course provides theoretical and practical knowledge on analyzing large and complex datasets using advanced data analytics techniques, developing machine learning models, and enhancing their interpretability through Explainable Artificial Intelligence (XAI). The course covers data preprocessing, feature engineering, supervised and unsupervised learning algorithms, model optimization, performance evaluation, and modern explainability techniques. Students gain hands-on experience with state-of-the-art interpretability methods, including SHAP, LIME, Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) plots, and counterfactual explanations. Using Python-based analytical tools and real-world datasets, students develop transparent, reliable, and high-performing machine learning models while acquiring the ability to interpret and communicate model outcomes for data-driven decision-making.