FIN 453

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Credit: 3 hours.

Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance, and make predictions. In this course, students will learn the foundations of Machine Learning and explore standard tools and algorithms. Topics include supervised learning (neural networks, regression trees, gradient boosting), unsupervised learning (clustering, principal component analysis), and introduction to reinforcement learning (Deep Q-Networks). Applications include option pricing, and credit card fraud detection. Students will gain practical experience implementing these models in Python with frequently used packages such as PyTorch, ScikitLearn and XGBoost.

3 undergraduate hours. No graduate credit. Prerequisite: MATH 220 or MATH 221 or MATH 234; MATH 227 or MATH 257; STAT 207; CS 307; FIN 321; FIN 411. Restricted to Finance or Finance + DS majors.

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