Computational thinking, machine learning, and quantitative finance for scientists and engineers — taught at Cornell and through eCornell professional certificates.
Teaches scientists and engineers to make quantitative financial decisions in corporate and wealth-management contexts, applying engineering, statistics, AI, and machine learning. Topics include quantitative asset-pricing models, risk management, and portfolio optimization using real-time and static data.
Integrates data science, statistics, linear algebra, artificial intelligence, and mathematical modeling using the Julia programming language. Students master software-engineering paradigms, data structures, and algorithms, with weekly labs applying machine learning to real-world industrial data.
An introduction to machine learning emphasizing supervised learning in engineering contexts: regularized linear models, boosting, kernel methods, deep learning, generative modeling, decision-making in stochastic systems, and reinforcement learning — implemented on real datasets.
Computational finance with Julia — analyzing financial instruments, modeling risk, optimizing portfolios, and building automated trading systems across fixed income, equities, options, and ML applications.
eCornell Program →Prepares financial professionals to apply AI — from fundamentals through investment research, credit risk, fraud detection, and client engagement, to responsible AI and organizational adoption, with Cornell faculty and industry practitioners.
eCornell Program →