Optimization and Computational Methods

Catalog description

This course introduces the quantitative and computational tools essential for modern financial analysis. Topics include probability theory, statistical inference, Bayesian analysis, structural estimation, convex and non-convex optimization, and predictive modeling. The course also explores foundational concepts in deep learning and large language models (LLMs). Students will gain hands-on experience implementing these methods using Python and standard libraries such as NumPy, Pandas, and Matplotlib. Special emphasis is placed on real-world financial applications, including portfolio optimization, macroeconomic forecasting, default modeling, and financial statement analysis. This familiarity with AI-driven tools for data analysis, model construction, and code generation, will prepare students to tackle challenges in today-s finance industry. Prerequisite(s): FIN 5752 and IDS 5420 .