coursia

CoursIA brings together educational resources organized into series of interactive notebooks. The course covers search and optimization, constraint satisfaction, symbolic reasoning, probability and decision-making, game theory, machine learning, reinforcement learning, generative AI, interdisciplinary case studies, and algorithmic trading with QuantConnect. The repository emphasizes multi-paradigm comparisons: the same problem, such as Sudoku, can be tackled through backtracking, constraint solvers, SMT, metaheuristics, probabilistic inference, neural networks, or a large language model. Many notebooks exist in twin Python and C# versions following the same progression, while Lean 4 is used for companion formal proofs. Reading is structured into three levels: numbered notebooks form the main path, notebooks with a letter deepen a stage, and sub-series open up research topics. Entry can be made by series, by narrative itinerary, by infrastructure constraint, or through the generated catalog. The latter provides counts, READY/DEMO statuses, and PRODUCTION/BETA maturity. Several paths work locally without an API key or GPU. Series requiring an API, Docker, WSL, the cloud, or a GPU document their environment in their getting-started notebook. The pedagogy alternates guided explanations, real outputs, and exercises to complete, and some series emphasize out-of-sample validation, multi-seed repetitions, walk-forward, Lean proofs, and documentation of negative results. The project is published under the MIT license.