Introduction to ML in Finance (FIN 453) 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.

Machine Learning in Finance (FIN 553) 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 state of the art algorithms and tools. Topics include supervised learning (neural networks, support vector machines), unsupervised learning (clustering, dimensionality reduction) and reinforcement learning (dynamic programming, Q-learning, SARSA, policy gradient methods). Applications include option pricing, portfolio selection and credit card fraud detection. Students will gain practical experience implementing these models in Python with frequently used packages such as TensorFlow.