This course examines Machine Learning (ML) and Artificial Intelligence (AI) within the context of natural and cultural learning processes. Beginning with information processing in primitive organisms, it traces the evolution of learning through neural systems, human societies, and modern computers. As foundational material, the course covers classic theories of learning and knowledge acquisition from cognitive science, philosophy, and psychology. Four main approaches to ML (supervised, unsupervised, reinforcement, and deep learning) are explored through applications such as medical diagnosis, cybersecurity, autonomous vehicles, and chatbots. Hands-on experiments with generative AI provide practical experience, as students reflect on human and machine learning processes.