CONCEPTS OF MACHINE LEARNING

Catalog description

This course introduces the practical concepts and contemporary applications of machine learning. It offers a broad, application-driven entry point designed to help learners apply and interpret machine learning methods without requiring deep mathematical preparation. Topics span supervised, unsupervised, generative, and reinforcement learning, emphasizing hands-on experience and model evaluation. Core machine learning architectures, such as neural networks and Transformers, are presented alongside milestone models that have shaped machine learning advances, such as generative and large-scale systems. Through structured assignments and a final project, participants will gain hands-on experience with widely used frameworks, critically assess model performance, and develop a practical understanding of both the potential and the limits of current AI technologies.