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.

This interdisciplinary course examines the notion of “truth” in the era of Artificial Intelligence (AI). It delves into the philosophical discourse on truth (e.g., correspondence, coherence, consensus, and pragmatism theories) and examines the scientific principles underlying logical reasoning. Students are challenged to reassess their assumptions about truth, as they analyse AI’s transformative power to learn and reason. They explore its implications across diverse disciplines (e.g., law, medicine, science, and humanities) with real-life cases. This foundational knowledge equips students with a good grasp of the complexities underlying truth, enhances their critical thinking and analytical skills to navigate advanced topics in AI.