Explore the fundamental question of why simple models are effective in physics and biology through this lecture on information geometry. Delve into topics like thermodynamics of information processing, coding in statistical mechanics, and quantum information in extended systems. Examine concepts such as the renormalization group, information theory applications, and the emergence of useful parameters in complex systems. Follow along as the lecturer covers graph theory, nonlinear squares, coordinate transforms, microscopic models, eigenvectors, the Ising model, diffusion equations, and partition functions. Gain insights into how information theory and computational complexity characterize low-energy landscapes and govern physical behaviors like glassy phenomena. Suitable for advanced graduate students, post-docs, and junior researchers in statistical physics, biological physics, and quantum information theory.