Ferroelectric materials are central to low‑power electronics, memory, and nanoscale devices, yet traditional design strategies face challenges from size scaling, depolarizing fields, and competing structural instabilities. This tutorial introduces modern approaches for ferroelectric and hyperferroelectric materials discovery that combine microscopic physical models, symmetry-based design principles, and physics-informed machine learning. We first review emerging mechanisms of polarization, including proper, hybrid improper, and hyperferroelectricity, and highlight how polarization can persist in reduced dimensions through structural coupling, strain, and chemical control. We then present computational workflows that integrate first-principles theory, phenomenological free-energy models, and lightweight ML strategies to rapidly screen candidate materials and predict stability and switching behavior. Central to this approach is the use of decoratypes, a site-based materials taxonomy that enables structure-aware discovery in data-scarce regimes. The talk emphasizes design rules and best practices for accelerating ferroelectric discovery for microelectronic applications while maintaining strong physical insight.