46-927 Machine Learning II

6 units, Carnegie Mellon University

The second in a two-course sequence covering statistical machine learning aimed at quantitative finance. The course further covers methods for regression and classification, along with other advanced topics in statistics and machine learning. Topics will be drawn from boosting and ensemble methods, clustering, mixture models and topic modeling, natural language processing, Markov decision processes and reinforcement learning, and neural networks/deep learning. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student, or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Heinz, Tepper, Computer Science Dept.,or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor.

Prerequisites: 46-921 and 46-926 and 46-923

Offered: Spring 2027, Spring 2026

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