The aim of this course is to teach students how to analyze complex datasets, develop predictive models, and interpret artificial intelligence outcomes using data science, statistical learning, and artificial intelligence techniques. Students will gain practical experience in data preprocessing, machine learning, model evaluation, and explainable artificial intelligence methods.

This course aims to provide both theoretical and practical knowledge on analyzing complex datasets, developing predictive models, and improving the interpretability of artificial intelligence models using data science, statistical learning, and artificial intelligence techniques. The course covers data preprocessing, exploratory data analysis, machine learning algorithms, model performance evaluation, and Explainable Artificial Intelligence (XAI) methods. Through hands-on applications with real-world datasets, students develop the skills to perform data-driven decision making, build predictive models, and effectively interpret AI-generated results.