Machine Learning.

Catalog description

Students explore the principles, design, implementation, and applications of machine learning (ML) algorithms and paradigms, enabling machines to learn from data. Topics may include traditional ML approaches, including supervised learning and unsupervised, as well as advanced practical techniques for feature engineering, matrix factorization, ensemble learning, and transfer learning. Emphasizing both theory and practice, students engage in hands-on exercises using open-source tools to build and evaluate ML models, gaining the computational and analytical skills needed to apply ML across diverse datasets in practical domains.